System and method for diagnosing arrhythmias and directing catheter therapies

ABSTRACT

An efficient system for diagnosing arrhythmias and directing catheter therapies may allow for measuring, classifying, analyzing, and mapping spatial electrophysiological (EP) patterns within a body. The efficient system may further guide arrhythmia therapy and update maps as treatment is delivered. The efficient system may use a medical device having a high density of sensors with a known spatial configuration for collecting EP data and positioning data. Further, the efficient system may also use an electronic control system (ECU) for computing and providing the user with a variety of metrics, derivative metrics, high definition (HD) maps, HD composite maps, and general visual aids for association with a geometrical anatomical model shown on a display device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/977,147, filed 28 Jun. 2013 (the '147 application), now U.S. Pat. No.9,186,081, which is a national stage application of internationalapplication no. PCT/US2011/066100, filed 20 Dec. 2011 (the '100application), now expired, which in turn claims the benefit of U.S.application No. 61/428,549, filed 30 Dec. 2010 (the '549 application),now expired. The '147 application, the '100 application and the '549application are each hereby incorporated by reference as though fullyset forth herein.

BACKGROUND OF THE INVENTION

a. Field of the Invention

The instant disclosure relates to a system employing a medical device,such as, for example, a catheter, for diagnostic, therapeutic, and/orablative procedures. More specifically, the instant disclosure relatesto a system for measuring, classifying, analyzing, and mapping spatialelectrophysiological patterns and for guiding arrhythmia therapy.

b. Background Art

The human heart muscle routinely experiences electrical currentstraversing its many surfaces and ventricles, including the endocardialchamber. Just prior to each heart contraction, the heart muscle is saidto “depolarize” and “repolarize,” as electrical currents spread acrossthe heart and throughout the body. In healthy hearts, the surfaces andventricles of the heart will experience an orderly progression of adepolarization wave. In unhealthy hearts, such as those experiencingatrial arrhythmia, including for example, ectopic atrial tachycardia,atrial fibrillation, and atrial flutter, the progression of thedepolarization wave may not be so orderly. Arrhythmias may persist as aresult of scar tissue or other obstacles to rapid and uniformdepolarization. These obstacles may cause depolarization waves to repeata circuit around some part of the heart. Atrial arrhythmia can create avariety of dangerous conditions, including irregular heart rates, lossof synchronous atrioventricular contractions, and stasis of blood flow,all of which can lead to a variety of ailments and even death.

Medical devices, such as, for example, electrophysiology (EP) catheters,are used in a variety of diagnostic and/or therapeutic medicalprocedures to correct such heart arrhythmias. Typically in a procedure,a catheter is manipulated through a patient's vasculature to a patient'sheart, for example, and carries one or more electrodes that may be usedfor mapping, ablation, diagnosis, and/or to perform other functions.Once at an intended site, treatment may include radio frequency (RF)ablation, cryoablation, lasers, chemicals, high-intensity focusedultrasound, etc. An ablation catheter imparts such ablative energy tocardiac tissue to create a lesion in the cardiac tissue. This lesiondisrupts undesirable electrical pathways and thereby limits or preventsstray electrical signals that lead to arrhythmias. As readily apparent,such treatment requires precise control of the catheter duringmanipulation to, from, and at the treatment site, which can invariablybe a function of a user's skill level.

Before or during an ablation procedure, however, a user must measure anddiagnose these undesirable electrical pathways and regions of arrhythmia“breakout.” An electrogram, used to help identify these regions, is anyrecord of change in electric potential over time, often obtained byplacing an electrode directly on or near the surface of the hearttissue. To acquire electrograms, conventional techniques includepoint-by-point methods of recording changes in electrical potential.These changes in potential may then be mapped onto a corresponding modelof an anatomical structure. In other words, these methods enable thecreation of electrocardiographic maps by navigating one or morecatheters around an area of interest and collecting electrogram andspatial localization data from one spot to the next and then mapping thecollected data accordingly.

A depolarization wave is detected on a signal from catheters to createmaps such as Local Activation Time (LAT) and Peak to Peak (PP) voltagemaps. In addition to being laborious and time consuming, these methodsassume that a mapped electrogram is the result of only onedepolarization. As a result, additional depolarizations, which commonlyoccur in complex arrhythmias, are not represented. Still further, due tothe sequential nature of data acquisition, each electrogram of interestmust then be time-aligned with a fixed fiducial reference.

Thus, conventional techniques and the resulting maps are not withoutdrawbacks. As a further example, one map that is often created is acomplex fractionated atrial electrogram (CFE) map. One type of CFE mapdocuments mean cycle length or activation interval over a one to eightsecond period. A primary limitation of this type of CFE map is with itslack of specificity. Although any given electrogram may demonstrate CFEpotentials, the underlying causes of the complex fractionated activityare unclear. And while the presence of complex fractionated activitysuggests underlying anisotropy of conduction, this type of CFE mapyields no direct information relating to underlying wavefrontpropagation patterns.

Accordingly, the inventors herein have recognized a need for improvedsystems and methods for acquiring a multitude of electrograms at thesame time, and for a system that can provide a user with spatial mapsthat enable the user to view electrophysiological patterns and todetermine the underlying causes of various arrhythmias that willminimize and/or eliminate one or more of the deficiencies inconventional systems.

SUMMARY OF THE INVENTION

It is desirable to identify the sources of cardiac arrhythmias based onelectrophysiological (EP) data, particularly for systems performingdiagnostic, therapeutic, and ablative procedures on a patient. EP datamay come from intrinsic rhythms such as, for example, Sinus Rhythm,Atrial Flutter, and Atrial Fibrillation. EP data may also come frommanual interventions such as pacing and induced arrhythmias, forexample. The present disclosure provides a system and methods formeasuring, classifying, analyzing, and mapping spatial and temporal EPpatterns. Based on analyses of collected EP data, the disclosed systemand methods also guide arrhythmia therapy by highlighting possiblesources of arrhythmia.

In one embodiment, the disclosed system can measure data from the tissueof a patient's body by using a plurality of sensors disposed along adistal end of a medical device. As noted above, one exemplary type ofdata that may be measured is EP data. Further, the medical device may bepositionable near, along, against, or within the tissue of the patient'sbody. One example of a sensor that may be used with the system is anelectrode. A high density of sensors may be disposed along the distalend of the medical device to simultaneously measure voltages withrespect to time (i.e., electrograms) from a region of the tissue.Because the sensors may be positioned proximal to one another andbecause the sensors may record data over a period of time, the disclosedsystem can perform a host of comparative spatial and temporal analyses.

The system may also comprise an electronic control unit (ECU) forcollecting and analyzing the data measured by the plurality of sensors.The ECU, which may itself comprise a number of sub-components, canperform many functions as part of the system. For example, the ECU mayacquire the measured data from the plurality of sensors positioned alongthe tissue. Using electric-field or magnetic-field based impedancetechniques, the ECU may also determine the three-dimensional positioncoordinates of each sensor. In addition, the ECU may “know” the spatialarrangement of the plurality of sensors before the medical device ispositioned near the tissue. In the alternative, the ECU may determinethe spatial arrangement of sensors by computing the distances betweeneach three-dimensional position coordinate. Accordingly, the ECU mayknow the positions and spatial arrangement of the sensors, the voltagesat each sensor, and the times at which those voltages were measured.Based on this input data, the ECU may compute a variety of metrics,derivative metrics, and combination metrics. Several examples of thecomputed metrics may include, without limitation, absolute activationtime (AAT), percentage fractionation index (PFI), continuous spatialindex (CSI), conduction velocity, spatial gradients of depolarizationamplitude, consistency metrics, and activation direction. Yet further,the ECU may also highlight areas of interest in the tissue based on theresults of these computed metrics. Using automated or semi-automated(computer-aided) intelligent compute algorithms and using a signalprocessing method (e.g., Hilbert transform) the disclosed system andmethod can be applied to characterize waveform patterns (e.g., rotor,focus beat, planar, dispersed) over the plurality of sensors.

In one embodiment, a method of operating the system to analyze data maybe described as follows. At least one of a plurality of sensorspositioned on a distal end of a medical device measures data from thebody tissue of a patient. The measured data may then be transmitted fromat least one of the plurality of sensors to an ECU. The system, or insome embodiments, the ECU, may then determine the position of theplurality of sensors, or at least the position of one sensor. The systemmay also compute at least one metric based on the position of the sensoror positions of the sensors. In general, metrics are quantifications ofdata such as, for example and without limitation, EP data. Based on theposition(s) of the sensor(s) and either the computed metric or themeasured data, the system may then generate a map for display. Aftercomputing these and other metrics, derivations, and combinationsthereof, the system may generate spatial maps of these metrics. In someembodiments, these maps may be superimposed onto a geometricalanatomical model representing the tissue. These spatial maps may beconfigured to be updated with each successive heartbeat, at thediscretion of the user, or continuously.

Another aspect of the disclosed system and methods involves identifyingdepolarization wavefront patterns based on the spatial distribution ofEP data or metric values. To do so, the ECU may be configured in oneexemplary embodiment to apply a variety of matched spatial filters tocompiled EP data values.

Still another aspect of the disclosed system and methods involvescreating three-dimensional visual aids representing either electrogramvoltage vectors or conduction velocity vectors. This type ofthree-dimensional display may be superimposed onto a geometricalanatomical model or may be displayed in a graphical format as well.

In general, the disclosed system and methods for measuring, classifying,analyzing, and mapping spatial EP patterns and for guiding arrhythmiatherapy are more spatially significant than ever before. The system andmethod provide physicians with more straightforward diagnosticinformation. Further, the metrics and maps described herein are merelyexemplary. The foregoing and other aspects, features, details,utilities, and advantages of the present disclosure will be apparentfrom reading the following description and claims, and from reviewingthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic and diagrammatic view of a system for performingat least one of a diagnostic and a therapeutic medical procedure inaccordance with present teachings.

FIG. 2 is an isometric view of a distal end of an exemplary embodimentof a medical device arranged in a spiral configuration.

FIG. 3 is an isometric view of a distal end of another exemplaryembodiment of a medical device arranged in a basket configuration.

FIGS. 4a-4b are isometric and a side views, respectively, of a distalend of an exemplary embodiment of a medical device arranged in amatrix-like configuration.

FIG. 5 is a top view of a distal end of an exemplary embodiment of amedical device wherein the medical device is a radio frequency (RF)ablation catheter.

FIG. 6 is a schematic and diagrammatic view of an exemplary embodimentof a visualization, navigation, and mapping subsystem of the systemillustrated in FIG. 1.

FIG. 7 is a representation of an exemplary embodiment of a displaydevice of the system illustrated in FIG. 1 with a graphical userinterface (GUI) displayed thereon.

FIG. 8a is a schematic representation of positioning coordinates ofsensors mounted on a medical device having a distal end arranged in aspiral configuration.

FIG. 8b is a schematic representation of raw positioning datacorresponding to sensors mounted on a medical device having a distal endarranged in a spiral configuration, wherein the raw positioning dataincludes one or more inaccuracies that portray a distorted spatialarrangement of sensors of the medical device.

FIG. 8c is a schematic representation of corrected positioningcoordinates corresponding to the raw positioning coordinates illustratedin FIG. 8 b.

FIG. 8d is a schematic representation of a high density gridcorresponding to the corrected positioning coordinates illustrated inFIG. 8 c.

FIG. 9 is an exemplary representation of electrograms and a dynamic mapillustrating an absolute activation time (AAT) metric in graphical formthat may be displayed on a display device of the system illustrated inFIG. 1.

FIG. 10a is an exemplary representation of electrograms and a mapdepicting a representation of a distal end of a medical deviceapproaching tissue to be measured that may be displayed on a displaydevice of the system illustrated in FIG. 1.

FIG. 10b is an exemplary representation of electrograms and a mapdepicting a conduction velocity metric, which shows cardiac directionand speed, with respect to an associated geometrical anatomical model.

FIG. 11 is a portion of an exemplary graphical user interface (GUI)showing a configuration of sensors, EP data as acquired from tissue, andelectrograms representative of the sensors.

FIG. 12 is a table showing several categories of exemplary wavefrontpatterns that a system uses to identify depolarization wavefronts onmeasured tissues.

FIG. 13 is an exemplary three-dimensional graph representing voltages inmilli-volts (mV) measured with respect to three different directions.

FIGS. 14a-14d are exemplary representations of electrograms and mapsshowing increments of data that are displayed by an electronic controlunit (ECU) as a distal end of a medical device measuringelectrophysiological data approaches tissue from a body.

FIG. 15 is another exemplary representation of a display device of thesystem illustrated in FIG. 1 with a graphical user interface (GUI)displayed thereon.

FIG. 16 is an exemplary representation of a surface map displaying afield of vectors with directional and magnitude significance displayedthereon.

FIG. 17 is a flow diagram showing one embodiment of a method in which asystem may measure, analyze, and map spatial electrophysiologicalpatterns.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings wherein like reference numerals are usedto identify identical components in the various views, FIG. 1illustrates one exemplary embodiment of a system 10 for performing oneor more diagnostic and/or therapeutic functions on or for a tissue 12 ofa body 14. In an exemplary embodiment, the tissue 12 comprises heart orcardiac tissue within a human body 14. It should be understood, however,that the system 10 may find application in connection with a variety ofother tissues within human and non-human bodies, and therefore, thepresent disclosure is not meant to be limited to the use of the system10 in connection with only cardiac tissue and/or human bodies.

The system 10 may include a medical device 16 and a subsystem 18 for thevisualization, navigation, and/or mapping of internal body structures(hereinafter referred to as the “visualization, navigation, and mappingsubsystem 18” or “subsystem 18”).

In the exemplary embodiment of FIG. 1, the medical device 16 comprises acatheter, such as, for example, an electrophysiology catheter. In otherexemplary embodiments, the medical device 16 may take a form other thana catheter, such as, for example and without limitation, a sheath orcatheter-introducer, or a catheter other than an electrophysiologycatheter. For clarity and illustrative purposes only, the descriptionbelow will be limited to embodiments of the system 10 wherein themedical device 16 comprises a catheter (catheter 16).

The catheter 16 is provided for examination, diagnosis, and/or treatmentof internal body tissues such as tissue 12. The catheter 16 may includea cable connector or interface 20, a handle 22, a shaft 24 having aproximal end 26 and a distal end 28 (as used herein, “proximal” refersto a direction toward the end of the catheter 16 near the handle 22, and“distal” refers to a direction away from the handle 22), and one or moresensors, such as, for example and without limitation, a plurality ofelectrodes 30 (i.e., 30 ₁, 30 ₂, . . . , 30 _(N)), mounted in or on theshaft 24 of the catheter 16 at or near the distal end 28 of the shaft24.

In an exemplary embodiment, each electrode 30 is configured to bothacquire electrophysiological (EP) data corresponding to the tissue 12,and to produce signals indicative of its three-dimensional (3-D)position (hereinafter referred to as “positioning data”). In anotherexemplary embodiment, the catheter 16 may include a combination ofelectrodes 30 and one or more positioning sensors (e.g., electrodesother than the electrodes 30 or magnetic sensors (e.g., coils)). In onesuch embodiment, the electrodes 30 are configured to acquire EP datarelating to the tissue 12, while the positioning sensor(s) is configuredto generate positioning data indicative of the 3-D position thereof,which, as will be described below, may be used to determine the 3-Dposition of each electrode 30. In other embodiments, the catheter 16 mayfurther include other conventional components such as, for example andwithout limitation, steering wires and actuators, irrigation lumens andports, pressure sensors, contact sensors, temperature sensors,additional electrodes and corresponding conductors or leads, and/orablation elements (e.g., ablation electrodes, high intensity focusedultrasound ablation elements, and the like).

The connector 20 provides mechanical and electrical connection(s) forone or more cables 32 extending, for example, from the visualization,navigation, and mapping subsystem 18 to the one or more electrodes 30 orthe positioning sensor(s) mounted on the catheter 16. In otherembodiments, the connector 20 may also provide mechanical, electrical,and/or fluid connections for cables extending from other components inthe system 10, such as, for example, an ablation system and a fluidsource (when the catheter 16 comprises an irrigated catheter). Theconnector 20 is conventional in the art and is disposed at the proximalend 26 of the catheter 16.

The handle 22 provides a location for a user to hold the catheter 16 andmay further provide means for steering or guiding the shaft 24 withinthe body 14. For example, the handle 22 may include means to manipulateone or more steering wires extending through catheter 16 to the distalend 28 of the shaft 24 to steer the shaft 24. The handle 22 is alsoconventional in the art and it will be understood that the constructionof the handle 22 may vary. In other embodiments, the control of thecatheter 16 may be automated such as by being robotically driven orcontrolled, or driven and controlled by a magnetic-based guidancesystem. Accordingly, catheters controlled either manually orautomatically are both within the spirit and scope of the presentdisclosure.

The shaft 24 is an elongate, tubular, and flexible member configured formovement within the body 14. The shaft 24 supports, for example andwithout limitation, the electrodes 30, other electrodes or positioningsensors mounted thereon, associated conductors, and possibly additionalelectronics used for signal processing or conditioning. The shaft 24 mayalso permit transport, delivery and/or removal of fluids (includingirrigation fluids, cryogenic ablation fluids, and body fluids),medicines, and/or surgical tools or instruments. The shaft 24, which maybe made from conventional materials such as polyurethane, defines one ormore lumens configured to house and/or transport electrical conductors,fluids, or surgical tools. The shaft 24 may be introduced into a bloodvessel or other structure within the body 14 through a conventionalintroducer. The shaft 24 may then be steered or guided through the body14 to a desired location such as the tissue 12 using means well known inthe art.

The distal end 28 of the shaft 24 may be the main portion of thecatheter 16 that contains the electrodes 30 or other sensors foracquiring EP data and positioning data. As described above, in oneembodiment, the electrodes 30 may be configured to acquire both EP dataand positioning data. In another embodiment, and as will be described ingreater detail below, the electrodes 30 may be configured to acquire EPdata while one or more positioning sensors may be configured to acquirepositioning data, which may then be used to determine the respectivepositions of the electrodes 30. Regardless of whether the positioningdata is acquired by the electrodes 30 or by positioning sensors, thedistal end 28 may be arranged in a number of configurations thatfacilitate the efficient acquisition, measurement, collection, or thelike of EP data from the tissue 12.

In one embodiment, as shown in FIG. 2, the distal end 28 may be arrangedin a spiral configuration. In this embodiment, the spiral configurationmay be generally planar and may contain a high density of electrodes 30for taking unipolar or bipolar measurements of EP data from the tissue12. Unipolar measurements may generally represent the electrical voltageperceived at each electrode. Bipolar measurements, though, may generallyrepresent the electrical potential between any pair of electrodes. Andas one skilled in the art will recognize, bipolar measurements may becomputed from unipolar measurements. Moreover, the electrodes 30 may bedisposed in or along the distal end 28 in a known spatial configurationsuch that the distances between the electrodes 30 are known. Thediameters of the loops, such as loop 52, may vary from one embodiment toanother. In one exemplary embodiment, the diameter of the outermost loopis twenty millimeters. In an alternative embodiment, the spiralconfiguration may contain multiple spiral loops.

There are many advantages to placing a high density of electrodes 30 onthe spiral configuration or at the distal end 28 of any catheter 16.Because the distribution of electrodes 30 is dense, and because of themultitude of possible unipolar and bipolar comparisons of the electrodes30, the spiral configuration may be ideal for creating high definition(HD) surface maps representative of electrical activity on the tissue12.

In another embodiment, as shown in FIG. 3, the distal end 28 may bearranged in a basket configuration. The basket configuration, or asimilar configuration with a generally cylindrical array of electrodes30, may contain a high density of electrodes 30. In one embodiment, theelectrodes 30 may be non-contact electrodes that generally need not bein contact with the tissue 12 to measure EP data. In another embodiment,the electrodes 30 may include both contact and non-contact electrodes.

Such non-contact electrodes may be used for unipolar analyses. It may beadvantageous to analyze unipolar EP data since a unipolar electrogrammorphology may provide more information regarding colliding wavefronts(presence of “R” waves in the QRS Complex known in the art), shortradius reentry wavefronts (presence of the sinusoid waveform), andsource wavefronts (a “QS” morphology on the electrogram at the onset ofdepolarization). In general, a depolarization wavefront is a group ofelectrical vectors that traverse the tissue 12 of the body 14. Asdescribed in more detail below, depolarization wavefronts may vary inpattern, size, amplitude, speed, and the like. And some depolarizationwavefronts may be relatively orderly while others may be relatively, oreven entirely, disorderly.

In another embodiment, however, bipolar EP data may provide betterspatial localization data, better depolarization wave directionalityindications, and better alternating current (AC) electrical noiserejection. With bipolar EP data, a pair of electrodes 30 (commonlyreferred to as “poles” or “bi-poles”) may be spaced apart, butpositioned relatively close together with respect to electric fieldscaused by other remote parts of the body 14. Thus, effects from remoteelectric fields may be negated since the electrodes 30 are positionedclose to one another and experience similar effects from the distantelectric field.

In yet another embodiment of the distal end 28 shown in FIGS. 4a-4b , amatrix-like configuration may also be provided with a high density ofelectrodes 30. FIG. 4a shows an isometric view of the matrix-likeconfiguration, while FIG. 4b shows a side view. The matrix-likeconfiguration may have a number of splines 72 arranged side by side,with each spline 72 having at least one electrode 30 mounted thereon.Longer splines may contain more electrodes 30 to maintain a consistentelectrode density throughout the matrix-like configuration.

In the embodiment shown in FIGS. 4a-4b , the matrix-like configurationmay be cupped, almost as if to have a slight scoop as seen in FIG. 4a .In another embodiment (not shown), the matrix-like configuration may besubstantially flat or planar, without any scoop-like feature. While bothembodiments may facilitate data measurements from the tissue 12, thematrix-like configuration shown in FIG. 4a in particular may be used toacquire at least some non-contact measurements. Another possible use ofthe matrix-like configuration would be to help diagnose arrhythmias anddirect epicardial ablation therapies in the pericardial space.

In one embodiment, the matrix-like configuration along with otherconfigurations of the distal end 28 may collapse to a streamlinedprofile for insertion, manipulation, and removal from the body 14. Inaddition, or in the alternative, the distal end 28 may be at leastpartially concealed and transported within the shaft 24 when notcollecting data or performing a procedure. The shaft 24 may be morestreamlined than the distal end 28, and therefore may provide a bettervehicle for transporting the distal end 28 to and from the tissue 12.Once at the intended site, the distal end 28 may be deployed from theshaft 24 to perform the intended procedures. Likewise, after theprocedures are performed, the distal end 28 may be re-concealed, atleast in part, within the shaft 24 for removal from the body 14.

One exemplary way in which the matrix-like configuration is collapsibleinto a streamlined profile or fully or partially deployable is to allowthe outer splines 72 to translate modestly within the shaft 24 whileanchoring the innermost splines 72 to the shaft 24 at a point 74 at thedistal end 28 thereof. Moreover, for enhanced functionality, a joint 76may be incorporated near the point 74, either for providing flexibilityor for selectively deflecting the distal end 28, thereby allowing thedistal end 28 better access to the tissue 12.

Another exemplary embodiment of a high-density electrode catheter isillustrated in FIG. 5. In this embodiment, the distal end 28 comprisesan ablation tip 80, and may be well suited for enhancing radio frequency(RF) ablation procedures. More particularly, the arrangement may allowfor the provision of rapid positioning feedback and may also enableupdates to be made to HD surface maps as the ablative procedures arebeing performed.

With continued reference to FIG. 5, in an exemplary embodiment wherein,as will be described below, the visualization, navigation, and mappingsubsystem 18 is an electric field-based system, the distal end 28 mayinclude a proximal ring electrode 30 _(A) positioned close to, yetspaced apart from, a series of spot or button electrodes 30 _(B). Theproximal ring electrode 30 _(A) and the spot electrodes 30 _(B) may beused to acquire both EP data and positioning data. Spaced furtherdistally from the spot electrodes 30 _(B) a distal ring electrode 30_(C) may be disposed in or on the shaft 24 so that bipolar measurementsof EP data may be made between the spot electrodes 30 _(B) and thedistal ring electrode 30 _(C). Finally, the distal end 28 furtherincludes an ablation electrode 82 for performing ablation therapies,such as, for example and without limitation, RF ablation therapies.

The visualization, navigation, and mapping subsystem 18 may determinethe positions of the proximal ring electrode 30 _(A) (or a geometriccenter thereof), the spot electrodes 30 _(B), and the distal ringelectrode 30 _(C) (or a geometric center thereof) in the same manner asthe position(s) of the electrode(s) 30 shown in FIG. 6, as will bedescribed in greater detail below. Based on these positions and/or theknown configuration of the distal end 28 (e.g., the spacing of thevarious electrodes), the position of the ablation electrode 82 may alsobe determined and, in certain embodiments, projected onto a geometricalanatomical model.

By incorporating at least three non-co-linear electrodes as isillustrated, for example, in FIG. 5, rotational information about thedistal end 28 (referred to as “orientation”) may be calculated. Hencesix degrees of freedom (three for position and three for orientation)may be determined for the ablation tip 80 of the catheter 16. Knowingthe position and orientation of the distal end 28 allows for a muchsimpler registration of coordinates into a body coordinate system, asopposed to a coordinate system with respect to the catheter itself.

In another embodiment wherein the visualization, navigation, and mappingsubsystem 18 comprises a magnetic field-based system, the distal end 28may include at least one magnetic field sensor—e.g., magnetic coils (notshown). If two or more magnetic field sensors are disposed near theablation electrode 82, a full six-degree-of-freedom registration ofmagnetic and spatial coordinates could be accomplished without having todetermine orthogonal coordinates by solving for a registrationtransformation from a variety of positions and orientations. Furtherbenefits of such a configuration may include advanced dislodgementdetection and deriving dynamic field scaling since they may be selfcontained.

In yet another embodiment of the distal end 28 illustrated in FIG. 5,the distal ring electrode 30 _(C) may be omitted and the spot electrodes30 _(B) may be located in its place. As a result, the spot electrodes 30_(B) would be closer to the ablation electrode 82, which would providepositioning coordinates closer to the ablation electrode 82. This inturn may provide for more accurate and precise calculation of theposition of the ablation electrode 82. Additionally, just as if thedistal ring electrode 30 _(C) were still in place, a mean signal fromthe spot electrodes 30 _(B) and the proximal ring electrode 30 _(A)could still be used to obtain bipolar EP data.

With reference to FIGS. 1 and 6, the visualization, navigation, andmapping subsystem 18 will now be described. The visualization,navigation, and mapping subsystem 18 is provided for visualization,navigation, and/or mapping of internal body structures and/or medicaldevices. In an exemplary embodiment, the subsystem 18 may contribute tothe functionality of the system 10 in two principal ways. First, thesubsystem 18 may provide the system 10 with a geometrical anatomicalmodel representing at least a portion of the tissue 12. Second, thesubsystem 18 may provide a means by which the position coordinates (x,y, z) of the electrodes 30 (or generally, sensors) may be determined asthey measure EP data for analyses performed as part of the system 10. Incertain embodiments, positioning sensors (e.g., electrical-field basedor magnetic-field based) that are fixed relative to the electrodes 30are used to determine the position coordinates. The positioning sensorsprovide the subsystem 18 with positioning data sufficient to determinethe position coordinates of the electrodes 30. In other embodiments,position coordinates may be determined from the electrodes 30 themselvesby using, for example, voltages measured by the electrodes 30.

The visualization, navigation, and mapping subsystem 18 may utilize anelectric field-based system, such as, for example, the ENSITE NAVX™system commercially available from St. Jude Medical., Inc. and asgenerally shown with reference to U.S. Pat. No. 7,263,397 titled “Methodand Apparatus for Catheter Navigation and Location and Mapping in theHeart,” the entire disclosure of which is incorporated herein byreference, or the ENSITE VELOCITY™ system running a version of the NAVX™software.

In other exemplary embodiments, the subsystem 18 may utilize systemsother than electric field-based systems. For example, the subsystem 18may comprise a magnetic field-based system such as the CARTO™ systemcommercially available from Biosense Webster, and as generally shownwith reference to one or more of U.S. Pat. No. 6,498,944 entitled“Intrabody Measurement”; U.S. Pat. No. 6,788,967 entitled “MedicalDiagnosis, Treatment and Imaging Systems”; and U.S. Pat. No. 6,690,963entitled “System and Method for Determining the Location and Orientationof an Invasive Medical Instrument,” the disclosures of which areincorporated herein by reference in their entireties.

In yet another exemplary embodiment, the subsystem 18 may include amagnetic field-based system such as the GMPS system commerciallyavailable from MediGuide Ltd., and as generally shown with reference toone or more of U.S. Pat. No. 6,233,476 entitled “Medical PositioningSystem”; U.S. Pat. No. 7,197,354 entitled “System for Determining thePosition and Orientation of a Catheter”; and U.S. Pat. No. 7,386,339entitled “Medical Imaging and Navigation System,” the disclosures ofwhich are incorporated herein by reference in their entireties.

In a further exemplary embodiment, the subsystem 18 may utilize acombination electric field-based and magnetic field-based system, suchas, for example and without limitation, the CARTO 3™ system alsocommercially available from Biosense Webster, and as generally shownwith reference to U.S. Pat. No. 7,536,218 entitled “HybridMagnetic-Based and Impedance Based Position Sensing,” the disclosure ofwhich is incorporated herein by reference in its entirety. In yet stillother exemplary embodiments, the subsystem 18 may comprise or be used inconjunction with other commonly available systems, such as, for exampleand without limitation, fluoroscopic, computed tomography (CT), andmagnetic resonance imaging (MRI)-based systems.

In an exemplary embodiment wherein the subsystem 18 comprises anelectric field-based system, and as described above, the catheter 16includes a plurality of electrodes 30 configured to both acquire EP dataand produce signals indicative of catheter position and/or orientationinformation (positioning data). The subsystem 18 may use, for exampleand without limitation, time-division multiplexing or other similartechniques such that positioning data indicative of the position of theelectrodes 30 is measured intermittently with EP data. Thus, an electricfield used to locate the electrodes 30 may be activated betweenmeasurements of EP data, and the electrodes 30 may be configured tomeasure both EP data and the electric field from the subsystem 18,though at different times.

In other exemplary embodiments, however, wherein the electrodes 30 maynot be configured to produce positioning data, the catheter 16 mayinclude one or more positioning sensors in addition to the electrodes30. In one such embodiment, the catheter 16 may include one or morepositioning electrodes configured to generate signals indicative of the3-D position or location of the positioning electrode(s). Using theposition of the positioning electrode(s) along with a knownconfiguration of the catheter 16 (e.g., the known spacing between thepositioning electrode(s) and the electrodes 30) the position or locationof each electrode 30 can be determined.

Alternatively, in another exemplary embodiment, rather than comprisingan electric-field based system, the subsystem 18 comprises a magneticfield-based system. In such an embodiment, the catheter 16 may includeone or more magnetic sensors (e.g., coils) configured to detect one ormore characteristics of a low-strength magnetic field. The detectedcharacteristics may be used, for example, to determine a 3-D position orlocation for the magnetic sensors(s), which may then be used with aknown configuration of the catheter 16 to determine a position orlocation for each electrode 30.

For purposes of clarity and illustration only, the subsystem 18 will bedescribed hereafter as comprising an electric field-based system, suchas, for example, the ENSITE NAVX™ or VELOCITY™ systems identified above.Further, the description below will be limited to an embodiment of thesystem 10 wherein the electrodes 30 are configured to both acquire EPdata and produce positioning data. It will be appreciated in view of theabove, however, that the present disclosure is not meant to be limitedto an embodiment wherein the subsystem 18 comprises an electricfield-based system or the electrodes 30 serve a dual purpose orfunction. Accordingly, embodiments wherein the subsystem 18 is otherthan an electric field-based system, and the catheter 16 includespositioning sensors in addition to the electrodes 30 remain within thespirit and scope of the present disclosure.

With reference to FIGS. 1 and 6, in an exemplary embodiment thesubsystem 18 may include an electronic control unit (ECU) 100 and adisplay device 102. Alternatively, one or both of the ECU 100 and thedisplay device 102 may be separate and distinct from, but electricallyconnected to and configured for communication with, the subsystem 18.The subsystem 18 may still further include a plurality of patchelectrodes 104, among other components. With the exception of a patchelectrode 104 _(B) called a “belly patch,” the patch electrodes 104 areprovided to generate electrical signals used, for example, indetermining the position and orientation of the catheter 16, and in theguidance thereof. The catheter 16 may be coupled to the ECU 100 orsubsystem 18 with a wired or wireless connection. A wireless connectionmay involve Bluetooth, Wi-Fi, or any other wireless communicationprotocol, for example, and may be more flexible than a wired connection.

In one embodiment, the patch electrodes 104 are placed orthogonally onthe surface of the body 14 and are used to create axes-specific electricfields within the body 14. For instance, patch electrodes 104 _(X1), 104_(X2) may be placed along a first (x) axis. Patch electrodes 104 _(Y1),104 _(Y2) may be placed along a second (y) axis, and patch electrodes104 _(Z1), 104 _(Z2) may be placed along a third (z) axis. These patchesmay act as a pair or dipole. In addition or in the alternative, thepatches may be paired off an axis or paired in series, e.g., 104 _(X1)is paired with 104 _(Y1), then 104 _(Y2), 104 _(Z1), 104 _(Z2). Inaddition, multiple patches may be placed on one axis, e.g., under thepatient. Each of the patch electrodes 104 may be coupled to a multiplexswitch 106. In an exemplary embodiment, the ECU 100 is configured,through appropriate software, to provide control signals to the switch106 to thereby sequentially couple pairs of electrodes 104 to a signalgenerator 108. Excitation of each pair of electrodes 104 generates anelectric field within the body 14 and within an area of interest such asthe tissue 12. Voltage levels at the non-excited electrodes 104, whichare referenced to the belly patch 104 _(B), are filtered and convertedand provided to the ECU 100 for use as reference values.

With the electrodes 30 electrically coupled to the ECU 100, theelectrodes 30 are placed within electrical fields that the patchelectrodes 104 create in the body 14 (e.g., within the heart) when thepatch electrodes 104 are excited. The electrodes 30 experience voltagesthat are dependent on the respective locations between the patchelectrodes 104 and the respective positions of the electrodes 30relative to the tissue 12. Voltage measurement comparisons made betweenthe electrodes 30 and the patch electrodes 104 can be used to determinethe position of each electrode 30 relative to the tissue 12.Accordingly, the ECU 100 is configured to determine position coordinates(x, y, z) of each electrode 30. Further, movement of the electrodes 30near or against the tissue 12 (e.g., within a heart chamber) producesinformation regarding the geometry of the tissue 12.

The information relating to the geometry of the tissue 12 may be used,for example, to generate models and/or maps (as will be described ingreater detail below) of anatomical structures that may be displayed ona display device, such as, for example, the display device 102.Information received from the electrodes 30 can also be used to displayon the display device 102 the location and orientation of the electrodes30 and/or the tip of catheter 16 relative to the tissue 12. Accordingly,among other things, the ECU 100 may provide a means for generatingdisplay signals for the display device 102 and for creating a graphicaluser interface (GUI) on the display device 102. It should be noted thatin some instances where the present disclosure refers to objects asbeing displayed on the GUI or the display device 102, this may actuallymean that representations of these objects are being displayed on theGUI or the display device 102.

It should also be noted that while in an exemplary embodiment the ECU100 is configured to perform some or all of the functionality describedabove and below, in another exemplary embodiment, the ECU 100 may beseparate and distinct from the subsystem 18, and the subsystem 18 mayhave another ECU configured to perform some or all of the functionalitydescribed herein. In such an embodiment, that ECU could be electricallycoupled to, and configured for communication with, the ECU 100. However,for purposes of clarity and illustration only, the description belowwill be limited to an embodiment wherein the ECU 100 is shared betweenthe subsystem 18 and the system 10 and is configured to perform thefunctionality described herein. Still further, despite reference to a“unit,” the ECU 100 may comprise a number or even a considerable numberof components (e.g., multiple units, multiple computers, etc.) forachieving the exemplary functions described herein. In some embodiments,then, the present disclosure contemplates the ECU 100 as encompassingcomponents that are in different locations.

The ECU 100 may include, for example, a programmable microprocessor ormicrocontroller, or may comprise an application specific integratedcircuit (ASIC). The ECU 100 may include a central processing unit (CPU)and an input/output (I/O) interface through which the ECU 100 mayreceive a plurality of input signals including, for example, signalsgenerated by the patch electrodes 104 and the positioning sensors 30.The ECU 100 may also generate a plurality of output signals including,for example, those used to control the display device 102 and the switch106. The ECU 100 may be configured to perform various functions, such asthose described in greater detail above and below, with appropriateprogramming instructions or code. Accordingly, in one embodiment, theECU 100 is programmed with one or more computer programs encoded on acomputer-readable storage medium 110 for performing the functionalitydescribed herein.

In addition to the above, the ECU 100 may further provide a means forcontrolling various components of the system 10 including, but notlimited to, the switch 106. In operation, the ECU 100 generates signalsto the control switch 106 to thereby selectively energize the patchelectrodes 104. The ECU 100 receives positioning data from the catheter16 reflecting changes in voltage levels and from the non-energized patchelectrodes 104. The ECU 100 uses the raw positioning data produced bythe patch electrodes 104 and the electrodes 30, and corrects the data toaccount for respiration, cardiac activity, and other artifacts usingknown or hereinafter developed techniques. The corrected data, whichcomprises position coordinates corresponding to each of the electrodes30 (e.g., (x, y, z)), may then be used by the ECU 100 in a number ofways, such as, for example and without limitation, to create ageometrical anatomical model of an anatomical structure or to create arepresentation of the catheter 16 that may be superimposed on a map,model, or image of the tissue 12 generated or acquired by the ECU 100.

The ECU 100 may be configured to construct a geometrical anatomicalmodel 120 of the tissue 12 for display on the display device 102, asshown in FIG. 7. The ECU 100 may also be configured to generate a GUI122 through which a user may, among other things, view the geometricalanatomical model 120. The ECU 100 may use positioning data acquired fromthe electrodes 30 or other sensors on the distal end 28 or from anothercatheter to construct the geometrical anatomical model 120. In oneembodiment, positioning data in the form of a collection of data pointsmay be acquired from surfaces of the tissue 12 by sweeping the distalend 28 of the catheter 16 along the surfaces of the tissue 12. From thiscollection of data points, the ECU 100 may construct the geometricalanatomical model 120. One way of constructing the geometrical anatomicalmodel 120 is described in U.S. patent application Ser. No. 12/347,216entitled “Multiple Shell Construction to Emulate Chamber Contractionwith a Mapping System,” the entire disclosure of which is incorporatedherein by reference. Moreover, the anatomical model 120 may comprise a3-D model or a two-dimensional (2-D) model. As will be described ingreater detail below, a variety of information may be displayed on thedisplay device 102, and in the GUI 122 displayed thereon, in particular,in conjunction with the geometrical anatomical model 120, such as, forexample, EP data, images of the catheter 16 and/or the electrodes 30,metric values based on EP data, HD surface maps, and HD compositesurface maps.

To display the data and images that are produced by the ECU 100, thedisplay device 102 may comprise one or more conventional computermonitors other display devices well known in the art. It is desirablefor the display device 102 to use hardware that avoids aliasing. Toavoid aliasing, the rate at which the display device 102 is refreshedshould be at least as fast as the frequency with which the ECU 100 isable to continuously compute various visual aids, such as, for example,HD surface maps.

As illustrated in FIG. 7, in an exemplary embodiment, the ECU 100 maygenerate a worldview upper torso 124 on the display device 102. Theworldview upper torso 124, which may be fully rotatable, may serve toinform or remind the viewer of the perspective in which the heart isbeing viewed, accessed, or manipulated—and hence where the distal end 28is located. For example, the worldview upper torso 124 may help theviewer identify the right ventricle from the left ventricle based onwhether a chest 126 or a back of the worldview upper torso 124 is shown.Other, similar icons may be used to the same purpose.

As described above, the plurality of electrodes 30 disposed at thedistal end 28 of the catheter 16 are configured to acquire EP data. Thedata collected by the respective electrodes 30 may be collectedsimultaneously. In one embodiment, EP data may comprise at least oneelectrogram. An electrogram indicates the voltage measured at a location(e.g., a point along tissue 12) over a period of time. By placing a highdensity of electrodes 30 on the distal end 28, the ECU 100 may acquire aset of electrograms measured from adjacent locations in the tissue 12during the same time period. The adjacent electrode 30 locations on thedistal end 28 may collectively be referred to as a “region.”

The ECU 100 may also acquire times at which electrograms are measured,the positions from which electrograms are measured, and the distancesbetween the electrodes 30. As for timing data, the ECU 100 may track,maintain, or associate timing data with the voltages of each electrode30 as measured. In addition, the 3-D position coordinates of eachelectrode 30 as it measures voltages may be determined, for example, asdescribed above by the visualization, navigation, and mapping subsystem18. The ECU 100 may be configured to continuously acquire positioncoordinates of the electrodes 30, especially when the electrodes 30 aremeasuring EP data. Because the ECU 100 may know the spatial distributionof electrodes 30 of each distal end 28 configuration (e.g., matrix-like,spiral, basket, etc.), the ECU 100 may recognize from the positioncoordinates of the electrodes 30 which configuration of the distal end28 is deployed within a patient. Furthermore, the distances between theelectrodes 30 may be known by the ECU 100 because the electrodes 30 maybe precisely and strategically arranged in a known spatialconfiguration. Thus, if the distal end 28 is not deformed, a variety ofanalyses may use the known distances between the electrodes 30 withouthaving to obtain the coordinate positions from the subsystem 18 to solvefor the distances between the electrodes 30.

With the ECU 100 having voltage, timing, and position data correspondingto the respective electrodes 30 in addition to the known electrode 30spatial configuration, many comparative temporal and spatial analysesmay be performed, as described below. Some of these analyses lead tocreation of HD surface maps representing activation patterns from thetissue 12, which are possible in part because of the high density of theelectrodes 30 at the distal end 28 of the shaft 24. By providing a highdensity of electrodes at the distal end 28, the accuracy and resolutionof HD surface maps produced by the system 10 are enhanced.

Moreover, one particularly beneficial aspect of the distal end 28 havinga high density of electrodes 30 is the robust nature in which itmeasures EP data suitable for bipolar comparisons by the ECU 100.Bipolar comparisons indicate the difference in voltage between twospaced-apart poles (i.e., electrodes 30) at a given point in time.However, not all bipolar arrangements capture all depolarization wavesthat traverse the tissue 12. For example, if a depolarization wave thatis parallel to a pair of electrodes 30 that are spaced apart across thedistal end 28 approaches and passes the pair of electrodes 30 at thesame time, a bipolar comparison of the pair of the electrodes 30 willnot indicate any difference in voltage due to the paralleldepolarization wave. With a high density of electrodes, though, a wavethat is parallel to one pair of electrodes 30 (i.e., two poles orbipoles of a bipolar arrangement) may not necessarily be parallel toother pairs of electrodes 30 (i.e., two poles or bipoles of a bipolararrangement) on the distal end 28. Therefore, the distal end 28 providesa more robust way of comparing bipolar EP data because a high density ofelectrodes 30 captures a wider variety of wavefront patterns.

In one embodiment, bipolar comparisons may be made from pairs ofelectrodes 30 along the distal end 28 where the pairs are angularlyspaced apart from one another. Having angularly spaced-apart electrodepairs on the distal end 28 readily provides for a determination ofwavefront direction regardless of an orientation of the distal end 28.Wavefront direction and other determinations are possible because thespatial distribution of sensors may be used to compute optimal bipolarelectrograms. For example, each sensor may be paired with all of itsneighboring sensors, and then the ECU 100 may adaptively select the mostnegative bipolar electrogram. The neighborhood may be defined based oneither the catheter model in the static fashion or with ENSITE NAVX™impedances in the dynamic fashion. The optimal bipolar electrogramincludes the traditional bipolar electrogram by definition, so theoptimal bipolar electrogram may provide even more diagnosticinformation. Optimal bipolar EP data subject to predefined functions(e.g., most negative, most positive) can be more sensitive to a changein the electric field. Thus, this enhanced EP signal may facilitate evenbetter cardiac activation detection.

Another advantage of having a high density of electrodes 30 at thedistal end 28 is that concentrated arrhythmias, and/or associatedsymptoms, side effects, or indications thereof, are more likely to bedetected. For example, short radius entry depolarization wavefrontpatterns, which are relatively small when compared to otherdepolarization wavefront patterns, may go unnoticed by traditionalcatheters or even catheters having multiple electrodes that are spaced,relatively speaking, too far apart. On the other hand, by taking intoconsideration the magnitude of even the smallest depolarizationwavefront patterns, the electrodes 30 at the distal end 28 may bespatially distributed to measure even the smallest depolarizationwavefront patterns.

With respect to capturing or collecting EP data measured by the highdensity of electrodes 30, in one embodiment, the ECU 100 may beprogrammed to continuously record and analyze data in real-time or nearreal-time. In another embodiment, a user may specify through a userinput device a time window (e.g., 200 ms, 20 seconds, etc.) during whichthe ECU 100 may capture data measured from the electrodes 30. The userinput device may include, for example and without limitation, a mouse, akeyboard, a touch screen, and/or the like. It should be noted that inone embodiment, the electrodes 30 may continuously measure voltagesalong the tissue 12, and the ECU 100 may selectively capture or recordsuch voltages from the electrodes 30. In still another embodiment, theelectrodes 30 measure voltages in accordance with a sampling rate orcommand from the ECU 100. Once the distal end 28 of the shaft 24 ispositioned near or along the tissue 12 as desired, the user could prompta trigger for the time window. The user may configure the trigger forthe time window to correspond, for example, to a particular cardiacsignal or the expiration of a timer. To illustrate, the trigger could beset so the ECU 100 records data from the electrodes 30 before, during,and after an arrhythmia breakout or disappearance. One possible way tocapture the data occurring just prior to the particular cardiac signalwould be to use a data buffer that stores data (which may later beobtained) for an amount of time.

As noted above, the ECU 100 may be configured to recognize particularcardiac signals to trigger the time window. To that end, the electrodes30 may constantly measure EP data when positioned near the tissue 12.This may be the case even if the user has not prompted the trigger forthe time window. For example, the ECU 100 may recognize that the distalend 28 is near tissue 12 inside the body 14 based on the continuousmeasurements in the range of voltages that are expected near the tissue12. Or the ECU 100 may, for example, be configured to constantly monitorvoltages from the electrodes 30 when the ECU 100 is powered “on.” In anyevent, the ECU 100 may continuously acquire EP data and continuouslyassess patterns and characteristics in the EP data. For example, the ECU100 may be programmed to continuously apply a matched filter onelectrograms recorded from certain predetermined electrodes 30 (e.g.,the electrodes 30 that are adjacent to each other).

To detect the presence of waveform patterns known to be associated withcertain arrhythmias, the matched filter may be used to compare a numberof these known waveform patterns with the waveforms of the electrogramsacquired by the electrodes 30. For example, based on prior experience,the peak-to-peak (PP) voltages associated with a particular arrhythmiamay be known. Even before the user prompts the system 10 to record data,the ECU 100 may compare the PP voltages of the particular arrhythmiawith the electrograms from the electrodes 30. Once the user prompts thesystem 10, the ECU 100 may record EP data from the electrodes 30 for thespecified time window when the ECU 100 next “sees” the known waveformpattern.

In one exemplary embodiment, as EP data is acquired, or after EP data isacquired, an assortment of analog or digital signal processing andconditioning instrumentation for equalizing, filtering, or generallyenhancing the characteristics of the acquired raw data, may be used. Insome instances, it may be desirable for the system 10 to calculatevarious gradient metrics, as discussed below. Gradients, though, areinherently noisy and demand densely sampled and clean data. While thehigh density of electrodes 30 disposed on the distal end 28 help inobtaining gradients, oftentimes raw data signals need refining.

By equalizing data signals, and in particular certain phases of datasignals, subsequent signal processing steps may become more robust. Onesuch equalization, for example, may be appropriate where a reliable,large amplitude signal originates from a repetitive reentrant arrhythmiathat repeats a circuit around some part of the tissue 12. In the case ofarrhythmia breakout, for example, the time interval of interest mayimmediately precede the breakout. Therefore, empiric or adaptiveadjustments may be made with respect to that particular time interval.As a further example, in the case of arrhythmia disappearance, the timeinterval of interest may immediately follow the disappearance. Likewise,analogous adjustments may be made.

Weak activation data in electrograms may also need to be amplified. Inone embodiment, electrogram signal gain may be altered with time toemphasize weak signals. In another embodiment, electrogram signalconditioning may vary with time to help emphasize rapid changes inamplitude, whether increases or decreases. Doing so may homogenizesignal strength for a more reliable determination of activation timing.In one embodiment, this may be accomplished by applying amultiplier-like operation. Moreover, additional high pass filtering maybe applied at the time when the signal is most likely to grow ordiminish using a state variable filter.

To further enhance the output that the system 10 and/or the ECU 100generate, a user, the system 10, and/or the subsystem 18 maycharacterize certain locations of the tissue 12 as represented by thegeometrical anatomical model 120. Once the anatomical model 120 iseither created or acquired by the visualization, navigation, and mappingsubsystem 18, certain locations may be flagged that are typically proneto, or have been identified in the past as, fostering arrhythmias(hereinafter “areas of interest”). Characterizing areas of interest intissue 12 may not necessarily be automated, although the system 10 orthe subsystem 18 may recognize some structural features of the tissue 12typically associated with certain electrical conductive characteristics.

Depending on skill level, the user may recognize and use the user inputdevice to mark particular locations having certain anatomicalcharacteristics known to affect functional characteristics (e.g.,electrical conductivity). For example, the user may identify locationsknown to constrain conduction, locations known to foster lowdepolarization amplitudes, locations known to exhibit particularconduction paths, or locations known to foster low conductionvelocities. These locations may feature certain anatomicalcharacteristics such as, for example, certain wall thicknesses,anatomical bundles, scars, smoothness, muscularity, and openings. Toillustrate, the user may mark a ridge or a junction where a vein and anappendage come together to form a common part of the heart wall. Imagingmodalities such as computed tomography (CT), magnetic resonance imaging(MRI), and ultrasound, for example, can also be used to identify suchlocations and/or anatomical characteristics.

If this information is provided, the system 10 may utilize thisinformation advantageously in the steps ahead. For example, the ECU 100may generate a cardiac anatomy metric based on this information and thenlater prompt a user to acquire more-than-usual amounts of EP data atthese locations. Also, the ECU 100 may consider these cardiac anatomymetrics when computing various metrics such as a consistency metric, forexample, from EP data acquired from these areas of potential interest.

In some embodiments, after tissue 12 characterizations, EP data,positioning data, and/or other forms of input have been collected, an HDsurface model may be constructed for registration to a geometricalanatomical model 120. The known spatial configuration of electrodes 30may be used, as shown in FIG. 8a , as a model 140 to correct for anyinaccuracies in raw measured data. In the example shown in FIG. 8a , theelectrodes 30, and therefore the distal end 28 of the catheter 16, areknown to be arranged in the spiral configuration. However, FIG. 8b showsposition coordinates as computed by the subsystem 18 from raw measureddata. Thus, because the distal end 28 is known not to be deformed asportrayed by the distorted positioning coordinates in FIG. 8b ,inaccuracies in the raw measured data likely exist.

The ECU 100 may apply a correction algorithm to obtain correctedposition coordinates based on the known coordinates from the model 140shown in FIG. 8a and/or the measured position coordinates shown in FIG.8b . One example of a correction algorithm is a least squares fittingalgorithm, which may result in a corrected 3-D affine transformation setof data 142, as represented in FIG. 8c . EP data values measured withthe electrodes 30 may also be correlated to the corrected positioncoordinates. Next, FIG. 8d shows how the ECU 100 may interpolate theaffine transformation data set 142 to obtain an HD grid 144. The ECU 100may interpolate both the corrected position coordinates of the affinetransformation data set 142 and the EP data values associated with eachcorrected position coordinate. As interpolated, the HD grid 144 providesa more refined set of data points with which to use in computing metricvalues and displaying surface maps and composite maps, etc.

Data from the fitting process provides additional information to verifysensor positions calculated from EP data. If the data is above apredefined tolerance, the measured coordinates are considered unreliableand thus corrected coordinates should not be used for the following dataanalysis. The fitting algorithm can also be used to detect if the distalend of the catheter has mechanical deformation. For example, an innerloop and an outer loop of the spiral configuration may be designed foralignment along a 2D plane. The distribution of sensors on the innerloop may be registered with the distribution of sensors on the outerloop using the fitting algorithm. If a normal of the 2D planeconstrained by the inner loop is significantly different from itscounterpart of the outer loop, the catheter may be deformed.

The ECU 100 may use the HD grid 144 in creating HD surface maps that areviewable on the display device 102. With an even higher density of datapoints, even greater resolution HD surface maps are possible. Theinterpolated data points on either the HD grid 144 or an HD surface mapmay be referred to as “vertices.”

One aspect of creating HD surface maps involves the ECU 100 using thecorrected and interpolated position coordinates. Another aspect involvesECU 100 using the interpolated EP data values associated with theinterpolated position coordinates. The ECU 100 may use the vertices fromthe HD grid 144 to associate the HD grid 144 to the known shape andpoints on the geometrical anatomical model 120. Associating may include,for example and without limitation, registering, fitting, matching, orotherwise superimposing. Once the HD grid 144 is associated with thegeometrical anatomical model 120 and various EP data or resultantcomputed metric values thereof are displayed in the form of a map, asdescribed below, the HD grid 144 may be more properly referred to as anHD surface map. The content of the HD surface map may depend on theinterests of a user. Also, HD surface maps may be recomputedcontinuously, or at least as fast as the ECU 100 will allow.

In an alternative embodiment, the HD grid 144 and HD surface maps may beconstructed without the prior construction of the geometrical anatomicalmodel 120. In other words, the geometrical anatomical model 120 orportions thereof may essentially be constructed concurrently with the HDgrid 144 and HD surface maps.

HD surface maps may reflect, among other things, the differences in thevalues or properties being represented at different locations on themap. One exemplary way in which the map may reflect these differences isby color coding. For example, the ECU 100 may be configured with a colorscheme that considers the range of resultant values from a computedmetric. Depending on the range of computed metric values, the ECU 100may be configured to assign each value or sub-range of values anappropriate color (e.g., purple to white). In the alternative, the usercould determine the range and/or scale of values that should bedisplayed in color. The color scheme could help highlight the range ofvalues across the map, whether from one metric or numerous metrics.Moreover, color coding portions of the HD surface map may communicatespatial variation of specific properties. Some of these specificproperties may include, for example, the resultant values of the metricsdescribed below, raw EP data, etc. Yet further, color coded maps may becontinuously updated such that the maps are dynamic and based onrecently or the most-recently measured EP data or derivations thereof.

As for the content of the HD surface maps, the ECU 100 may be configuredto compute one or more metrics, derivation metrics, and combinationmetrics (generally “metrics”) and to display the values of those metricson the HD surface maps or HD “composite” surface maps. Metrics aregenerally various quantifications of EP data, and maps of resultantmetric values spatially depict these quantifications. In addition,metrics may refer to the quantification of EP data acquired from one ormore electrodes 30.

Certain metrics based on EP data are well known in the art. Theseinclude, for example, local activation time (LAT), depolarizationamplitude voltage (e.g., peak-to-peak amplitude (PP)), complexfractionated electrogram (CFE) activity, dominant frequency (DF), andFast Fourier Transform (FFT) ratio. An LAT metric represents thedifference in time between when a stationary reference electrodeexperiences a depolarization wavefront and when one or more rovingelectrodes (electrodes that are swept over or around the tissue 12)experience the depolarization wavefront. A PP metric represents anamount of change between the highest peak voltage and the lowest troughvoltage experienced by a specific point on the tissue 12 during adepolarization wave. A CFE metric is described in U.S. Pat. No.8,038,625 titled “System and Method for Three-Dimensional Mapping ofElectrophysiology Information,” the entire disclosure of which isincorporated herein by reference. A DF metric represents the mostdominant frequency in a power spectrum analysis of a given interval ofcardiac signal.

As described above, the system 10 is particularly efficient in obtainingEP data for these and other metrics because of the high density ofelectrodes 30 that can simultaneously measure EP data. Moreover, becausethe electrodes 30 of the distal end 28 can simultaneously measure EPdata from the tissue 12, the system 10 does not have to time alignsignals measured from different locations at different points in time.Because of this, the system 10 provides significant temporal and spatialcapabilities. Further, because of these capabilities, the ECU 100 mayuse metrics that are known in the art and/or the metrics described belowto derive more-advanced metrics, to combine metrics, and/or to analyzecomplex arrhythmias.

In an exemplary embodiment, the ECU 100 may be configured to calculatevalues for additional metrics based at least in part on EP datacollected by the electrodes 30. These metrics may include, for exampleand without limitation, an absolute activation time (AAT), a percentagefractionation index (PFI), a continuous spatial index (CSI), aconduction velocity vector, spatial gradients of depolarizationamplitude, consistency metrics, and metrics based on a combination oftwo or more metrics, to name a few. Each of these metrics will bedescribed in turn below. Although the following metrics may be describedwith reference to the electrodes 30 on the distal end 28, the ECU 100can perform the same computations between vertices of the HD grid 144after EP data and position coordinates have been interpolated.

Before proceeding with a description of these exemplary metrics, severalterms should be explained in more detail. Variations of the term“depolarization” may have a range of meanings. In some exemplaryembodiments, locations in the tissue 12 where the electrodes 30 arestationed (and where electrograms associated with the electrodes 30 areacquired) may be said to be “depolarizing” as the depolarization wave ispassing. After the wave passes, these locations in the tissue 12 and theelectrodes 30 may be said to have been “depolarized” or “activated.”

With reference to FIG. 9, the AAT metric will be described. A value ofthe AAT metric is calculated for one or more of the electrodes 30, asshown collectively by elapsed times 150. AAT values are indicative of anamount of time that has elapsed since a most recent activation at eachelectrode 30. Depolarization waves “activate” or “depolarize” differentelectrodes 30 (positioned at locations along the tissue 12) at differenttimes. In other words, the AAT value for a particular electrodeindicates the amount of time that has elapsed since a most-recentdepolarization wave passed the particular electrode. A group ofdepolarizations occurring around a point in time 152 are shown for aplurality of electrodes in the electrogram waves illustrated in FIG. 9.The elapsed times 150 may be measured with reference to a position of anautomated cursor 154 on the GUI 122 or, more generally, on the displaydevice 102. The position of the cursor 154 corresponds to a point intime. In an alternative embodiment, the position of cursor 154 is notautomated. A user may select the cursor 154 on the GUI 122 and move itin relation to the electrograms. Moving the cursor 154 to the left wouldcorrespond to an earlier point in time, while moving the cursor 154 tothe right would correspond to a later point in time. Depending on theposition of the cursor 154, the AAT metric value for each electrode 30will change along with a corresponding spatial map of these values.

The dynamic map shown in FIG. 9 illustrates AAT computed for eachbipolar electrogram captured from the electrodes 30 at the distal end28. In this embodiment, the distal end 28 is in the spiral configurationand has twenty electrodes 30, each of which has its own AAT, though onlyten of the corresponding electrogram waves are shown. The AATs for thetwenty electrodes 30 are shown in the elapsed times 150. Each electrode30 may have an identifier, such as “30 _(AA),” for example, whichdistinguishes it from the other nineteen electrodes 30 disposed on thedistal end 28 in this embodiment. Based on the elapsed times 150 shown,the electrode 30 _(OO) has an AAT of (−6) milliseconds. The electrode 30_(GG) has an AAT of (−34) milliseconds. Thus, a depolarization wavepassed the electrode 30 _(OO) six milliseconds prior to a point in timecorresponding to the position of the cursor 154. And a depolarizationwave passed the electrode 30 _(GG) thirty-four milliseconds prior to thesame point in time corresponding to the position of the cursor 154.

Unlike conventional maps such as LAT and PP maps, the dynamic AAT mapmay allow a user to identify and analyze multiple depolarizationwavefronts evident in certain arrhythmias. The dynamic AAT map in FIG. 9may be color coded with a legend 156 to indicate the times elapsed fromactivation, with each color region 158 corresponding to a differentrange of times. One of ordinary skill in the art will recognize arelatively planar wavefront from a left part 160 of the map display to aright part 162. Specifically, the left part 160 of the map showsactivation times in the range of minus 20-30 milliseconds while theright part 162 of the map shows activation times in the range of minus0-10 milliseconds.

Further, as described above, the cursor 154 may represent the currenttime of interest, whether determined by a user or automatically by theECU 100. As the cursor 154 changes positions, the coloring of the mapmay change indicating cardiac wavefront direction. From the perspectiveof each electrode 30, when a depolarization wave from any source passes,the AAT for each respective electrode 30 may reset to zero. Thus, withnormal EP wavefront activity, the AAT values of the electrodes 30 mayreset in a relatively orderly progression. In a state of arrhythmia,however, the resetting of AAT values associated with the electrodes 30may not be orderly.

Another metric for which the ECU 100 may calculate a value is thepercentage fractionation index, or PFI. The PFI may represent therelative amount of time that an electrogram, which may be acquired by asingle electrode at a single site, spends depolarizing during atimeframe. In one embodiment, the ECU 100 may determine a timeframe ofinterest. For example, the ECU 100 may begin to capture EP data from theelectrodes 30 when electrical voltages measured from the tissue 12exhibit characteristics of arrhythmia. In another embodiment, a user canspecify the timeframe. PFI can be described with reference to theelectrograms displayed in a lower viewing pane 164. Taking again theelectrogram labeled “EGM 30 _(GG)” corresponding to the electrode 30_(GG), for example, a PFI could be calculated based on the amount oftime that this electrogram spends depolarizing during the timeframe ofinterest. Here, the timeframe is shown to be 2000 milliseconds, becausethe units along the X-axis are number of 1200 Hz samples, where 1200 Hzis equivalent to 1000 milliseconds and 2400 Hz is equivalent to 2000milliseconds. The electrogram EGM 30 _(GG) experiences sixdepolarizations, one of which is shown near the time 152, during thistimeframe. If it is assumed that each depolarization occurs over a 60millisecond interval, the electrogram will spend 360 millisecondsdepolarizing during this 2000 millisecond timeframe. PFI may berepresented as a percentage or in milliseconds. Thus, the PFI for thiselectrogram during the timeframe may be represented as 360 millisecondsor, in the alternative, eighteen percent of the overall evaluationtimeframe.

The PFI value for a piece of tissue 12 becomes particularly helpful whencompared to the PFI values of other electrograms. The ECU 100 or a usermay compare the measured and computed PFI value, for example, with PFIvalues computed from other electrograms measured from the tissue 12,with PFI values measured from locations on the tissue 12 where no stateof arrhythmia is present, or with PFI values measured from tissue in ahealthy state. In short, areas of interest on the tissue 12 are likelyto experience more depolarization wavefronts than are healthy areas ontissue 12. Areas of interest on the tissue 12, therefore, are alsolikely to spend a larger percentage of time depolarizing than locationsthat do not accommodate arrhythmias. Thus, the higher the PFI value, themore likely it is that the location from which that PFI value wascomputed is experiencing an arrhythmia.

Hence, if the PFI value for healthy heart tissue is much lower thantwenty percent, this may indicate that the location from whichelectrogram EGM 30 _(GG) was calculated is an area of interest. Onaverage, healthy tissue may spend 60 milliseconds depolarizing perheartbeat, and on average, healthy tissue may experience 80 heart beatsper minute. This would suggest that healthy tissue spends about 160milliseconds

$\left( {\frac{80\mspace{14mu}{hb}}{1\mspace{14mu}\min} \times \frac{1\mspace{14mu}\min}{60000\mspace{14mu}{ms}} \times \left( {2000\mspace{14mu}{ms}} \right) \times \left( {60\mspace{14mu}{ms}} \right)} \right)$depolarizing during a 2000 millisecond window. In this example with theEGM 30 _(GG), then, it is likely that the electrogram EGM 30 _(GG) wasmeasured from an area of interest since it spends almost double thenormal amount of time depolarizing. A user may control, for a variety ofpurposes, default values such as how long healthy tissue spendsdepolarizing, healthy PFI values, etc.

Yet another metric that the ECU 100 may compute is the continuousspatial index, or CSI. The CSI, which is similar to the PFI, is thesummation of the amount of time that at least one electrogram of a setspends depolarizing during a time window. In one exemplary embodiment, aset may be defined by electrograms acquired from the electrodes 30disposed along the distal end 28 of the catheter 16. In otherembodiments, a set may include some other combination of electrograms.For example, a set may include electrograms measured from positioningthe distal end 28 along several adjacent locations on the tissue 12,particularly if these locations are suspected areas of interest. As afurther example, the set may comprise only some of the electrogramsmeasured from the distal end 28. In still other embodiments, the set maycomprise one or more electrodes from numerous catheters.

The CSI metric may be particularly helpful in identifying certain typesof wavefront patterns, which are described below. For example, a set ofelectrograms experiencing normal, planar depolarization wavefronts willhave a relatively low CSI percentage value. This may be true especiallywhere the electrodes 30 measuring the electrograms are aligned such thatnumerous electrodes 30 experience the depolarization wave at roughly thesame time. But on the other hand, at least one electrogram (orelectrode) from a set of electrograms that is experiencing a generallycircular wavefront pattern will be constantly depolarizing. Thus, thislatter set of electrograms may have a CSI value near 100 percent.Therefore, like PFI, a high CSI value suggests an area of interest.

The ECU 100 may also compute a conduction velocity metric. The values ofthis metric may be mapped to show 2-D vectors representing the directionand conduction velocity of underlying depolarization wavefronts in thetissue 12. Each electrode 30 could have a depolarization wavefrontvelocity direction and speed computed and displayed, if desired.Moreover, the ECU 100 may be configured in one embodiment to generate amap of the conduction velocity metric as soon as the distal end 28 ispositioned sufficiently close to the tissue 12. To determine theproximity of the distal end 28 to the tissue 12, the ECU 100 mayutilize, for example, ENSITE CONTACT™ technology and Euclidean distancebetween the electrodes 30 and a geometrical anatomical model, such asmodel 120 of FIG. 7.

However, when the distal end 28 is not sufficiently close to the tissue12, the ECU 100 may prevent the display of the conduction velocitymetric values because the electrodes 30 may not be in a position toacquire quality EP data or a sufficient amount of quality EP data. FIG.10a shows a representation 180 of the distal end 28 without anyconduction velocity vectors. The representation 180 may be displayedwhen the distal end 28 approaches the tissue 12, but is too remote tomeasure quality data. As the distal end 28 is moved closer to the tissue12, resultant values from the conduction velocity metric may bespatially mapped on the geometrical anatomical model 120 in the form ofan HD surface map 182, as shown in FIG. 10b . More particularly,indicators representative of the velocity and direction of the wavefrontmay be superimposed onto the geometrical anatomical model 120, asdescribed below. Precluding updates or the display of conductionvelocity vectors until the distal end 28 is sufficiently close to thetissue 12 is just one of many measures of quality control throughout thesystem 10.

In any event, the ECU 100 may use the coordinate positions of theelectrodes 30 and the times at which depolarization waves pass theelectrodes 30 to compute the conduction velocity metric. In the casethat the distal end 28 is not deformed, the ECU 100 would not need thecoordinate positions of the electrodes 30 to compute conduction velocityand may instead use the known distances between the electrodes 30arranged in the known spatial configuration. The times at which peaks ofdepolarization waves pass the electrodes 30 may be determined fromcorresponding electrograms by employing signal processing techniquesknown in the art.

The system 10 can determine the direction at which a wavefront passes aspecific electrode 30 on the distal end 28 by comparing activation timesof neighboring electrodes 30. The result of this determination, ifmapped, would appear like arrows 184 on a top right rendering window 186shown in FIG. 11. This capability of the system 10 is attributable, atleast in part, to the high spatial density of electrograms acquired bythe ECU 100.

For example, the activation time of an electrode 30 that is positionedcentrally to five surrounding electrodes 30 may be compared to theactivation times of the five surrounding electrodes 30. The system 10may then determine a path along which a wavefront is heading byidentifying the surrounding electrode 30 having an activation timeclosest to the activation time of the centrally-positioned electrode 30.The wavefront will likely be traveling along the determined path towardsthe electrode 30 that has the later activation time (of the surroundingelectrode 30 and the centrally-positioned electrode 30). As forelectrodes 30 on the outermost loop of the distal end 28 in the spiralconfiguration, data may need to be acquired from nearby locations beforesuch a determination is made.

In another example, the ECU 100 may compute the speed with which thedepolarization wave travels from a first electrode (e₁) to a secondelectrode (e₂). The speed between e₁ and e₂ can be formulated by avector in the 3D coordinate system. The direction, then, is defined as:

${\overset{\rightarrow}{N} = \frac{\overset{\rightarrow}{V}}{\left| \overset{\rightarrow}{V} \right|}},{where}$$\overset{\rightarrow}{V} = {{\left( {{x_{e_{2}} - x_{e_{1}}},{y_{e_{2}} - y_{e_{1}}},{z_{e_{2}} - z_{e_{1}}}} \right)\text{/}t_{e_{2}}} - t_{e_{1}}}$is the cardiac conduction speed measured between e₁ and e₂. In FIG. 11,the arrows 184 on the top right rendering window 186 represent thedirection of cardiac activity at one snapshot. The arrows can be groupedin space (e.g., per quadrant and the whole catheter). These arrows 184are consistent with the AAT color mapping discussed above.

In the alternative, the ECU 100 may compute the velocity with which thedepolarization wave travels from the e₁ to e₂ using the followingequation:

${{Velocity}_{e_{1}\rightarrow e_{2}} = {\left| \overset{\rightarrow}{V} \right| = \frac{\sqrt{\left( {x_{e_{2}} - x_{e_{1}}} \right)^{2} + \left( {y_{e_{2}} - y_{e_{1}}} \right)^{2} + \left( {z_{e_{2}} - z_{e_{1}}} \right)^{2}}}{t_{e_{2}} - t_{e_{1}}}}},$the ECU 100 may divide the distance between the first electrode (e₁) andthe second electrode (e₂) by the difference between a first time (t_(e)₁ ) at which a depolarization wave passes the first electrode (e₁) and asecond time (t_(e) ₂ ) at which the depolarization wave passes thesecond electrode (e₂). By computing this metric for all adjacentelectrodes 30 of a measured region, the ECU 100 can determine thevelocities with which depolarization waves travel between multiplepoints on the tissue 12. In an alternative embodiment, conductionvelocity values may also be computed from a temporal gradient ofactivation time.

Still other metrics may involve spatial and temporal gradients.Gradients are inherently noisy and demand densely sampled and cleandata. Thus, the ability to compute gradients is largely possible becauseof the high density of electrodes 30 disposed on the distal end 28.Spatial gradients aim to identify locations where there is an abruptchange in scalar quantity over a spatial range, while temporal gradientsaim to identify locations where there is an abrupt change in scalarquantity over a given time.

One example of a 2-D temporal gradient involves computing the derivativeof a conduction velocity metric with respect to time. One resultantvalue of the measured region is shown generally by

$\frac{\mathbb{d}\left( v_{1,2} \right)}{\mathbb{d}(t)},$where t represents a change in time and v_(1,2) represents conductionvelocity between a first electrode location and a second electrodelocation. This resultant value may represent the acceleration ordeceleration of a depolarization wave traveling between two electrodes30, one at the first electrode location and one at the second electrodelocation. As with most other metrics, the ECU 100 may be configured tocompute this derivative metric for all the interpolated data of the HDgrid 144, and then display the resultant values in the form of an HDsurface map associated to the geometrical anatomical model 120 fordisplay on the display device 102. An area showing a high rate of changemay be an area of interest. For example, this area may represent aportion of the tissue 12 having scar tissue. Similar computations can beused to compute spatial gradients.

Two gradients of particular importance include spatial gradients ofdepolarization PP (peak-to-peak) amplitude and activation time. Aspatial gradient of depolarization PP amplitude originates from adepolarization PP amplitude metric or an optimal bipolar electrogram. Adepolarization PP amplitude metric may indicate the PP amplitude that anelectrode, such as one of the electrodes 30, measures during eachdepolarization at a location in tissue 12. Spatially mapping theresultant values of this metric could show the spatial distribution ofPP amplitudes in a region based on multiple electrodes 30. In oneexemplary embodiment, the ECU 100 may consistently update an HD surfacemap on the display device 102 with each new PP amplitude that is lessthan a threshold value filter.

Further, mapping the spatial gradient of depolarization PP amplitude mayalso show the change in PP amplitude at each electrode 30 (or vertex ofHD grid 144) with each sequential depolarization wave. Areas of thetissue 12 experiencing relatively consistent PP depolarizationamplitudes will show minimal values, if any, when the spatial gradientof PP depolarization amplitude is mapped. By contrast, areas of thetissue 12 experiencing considerable change in PP depolarizationamplitudes will show high values on the HD surface map. Theseheterogeneous areas may be areas of interest. Similar gradient metricsmay be computed for activation times, and other characteristics of thetissue 12.

Another metric that the ECU 100 may be configured to calculate is aconsistency metric. The consistency metric generally indicates whetherresultant data from a primary metric is consistent or not. Many metricsexist from which to calculate the consistency metric. The consistencymetric may be used to determine stationary characteristics of theelectrogram depolarization and cardiac wavefronts. This metric may, insome embodiments, be calculated from the standard deviation (std)equation shown below:

${{std} = \sqrt{\frac{{\Sigma\left( {S - \overset{\_}{S}} \right)}^{2}}{N}}},$where “S” represents the natural log value of each computed resultantvalue from the primary metric, “S” is the mean of the natural log valuesof the calculated resultant values, and “N” is the number of calculatedscale values. Another way to express this consistency metric is with therelative standard deviation, which is defined as std divided by the meanof the log values, can also be used for consistency analysis. Therelative standard deviation may be more meaningful and precise tocompare different measurements than std.

Moreover, the consistency metric may be computed by spatial grouping orby temporal grouping. For example, the set of computed resultant valuesfrom which “S,” “S,” and “N” are obtained may be the PP depolarizationamplitude metric values measured at an instant in time. Hence thecomputed standard deviation would represent the deviation in valuesacross the spatial region from which the PP depolarization amplitudemetric was computed. In another example, the set of scalar values fromwhich “S,” “S,” and “N” are obtained may involve a series of timesequential PP depolarization amplitude metric values from one particularelectrode 30 on the distal end 28. Hence the computed standard deviationwould represent the deviation in values produced by one electrode 30,and thus one location in tissue 12, over a period of time. In a furtherexample, combinations of spatial groupings and temporal groupings may becombined.

Let [S₁ ^(t), . . . , S_(M) ^(t)]^(T) be a column vector representingthe metric values of M sensors at time t. The ECU 100 may sample thesystem at different times, for example, t₁, . . . t_(N). Combining all Nsamples yields a M×N matrix:

$s = {\begin{bmatrix}s_{1}^{t_{1}} & . & . & . & s_{1}^{t_{N}} \\. & . & . & . & . \\. & . & . & . & . \\. & . & . & . & . \\s_{M}^{t_{1}} & . & . & . & s_{M}^{t_{N}}\end{bmatrix}.}$The ECU 100 may apply a principal component analysis to the matrix. Theeigenvaules of the matrix may indicate how stable the measurements willbe over time. Ideally, only the first eigenvalue will be nonzero becausethe rank of the matrix is one. The ECU 100 may compare the firsteigenvalue with the summation of the other eigenvalues. The moredominant the first eigenvalue, the more consistent the measurement.

More generally, EP data that leads to resultant metric values having alow consistency may indicate an area of interest. Resultant metricvalues having a low consistency suggest that at least one aspect of theEP data may change with each successive depolarization wave. Forexample, a consistency metric may be computed based on a series of LATmetric values from a set of electrodes 30. If the resultant LAT valueshave a high consistency, or in other words, a low standard deviation,this means that the LAT values remain fairly constant from onedepolarization to the next. If, however, the resultant LAT values have alow consistency, or in other words, a high standard deviation, thismeans that the LAT values are fluctuating from one depolarization to thenext. Accordingly, a user may want to further probe areas of interestproviding low consistency resultant data.

In one embodiment, the consistency metric may be computed as an HDsurface map to be associated with the geometrical anatomical model 120,particularly where a consistency metric is computed for each electrode30 location. In another embodiment, the consistency metric may be shownas an additional legend or statistic appearing near an HD surface map.

Another aspect of the system 10 concerns its ability to algorithmicallycombine two or more metrics computed from the same area of tissue 12 toform a composite map. All of the aforementioned metrics, even standingalone, may be very helpful in locating areas of interest. Under certainconditions, though, it may be even more valuable to combine two or moreof these metrics to form a composite map. Composite maps, whichoriginate in some embodiments from combining the resultant data from twoor more metrics, may show where metrics reinforce one another where theyare largely in agreement. For example, two gradients may mutuallyreinforce one another where their vectors largely agree on (a) thevelocity and direction of activation, and (b) the rate of rise ofamplitude near a breakout site. In some embodiments, the resultantvalues of combined metrics may be associated with the geometricalanatomical model 120. In other embodiments, the resultant values mayserve as indicia of confidence, consistency, or validity of othercomputations.

A wide variety of metrics can be combined to provide further insightregarding the tissue 12. For example, combining several spatialgradients having high rates of amplitude change may be predictive ofarrhythmia initiation and termination sites since these combinationshighlight regions of rapidly growing amplitude (arrhythmia breakout orfocal origination) or rapidly shrinking amplitude (arrhythmiadisappearance, break-in, or block). As a further example, combining aspatial distribution of activation amplitude and a temporal pattern ofdepolarization may facilitate the identification of reentrant or ectopicarrhythmia sites. Still another example involves combining at least twohigh spatial gradients of amplitude to show arrhythmia initiation andtermination. Yet other examples include combining electrogram amplitudewith a temporal gradient of depolarization, combining early activationtime and lowest voltage, and combining LAT and PP metrics.

One possible way to combine scalar metrics into a composite map is tonormalize the data, weight the data, and combine the data to form acomposite map. This can be done even if the resultant data values fromthe primary metrics are in different units. A user or the ECU 100 mayselect ranges (e.g., low to high) for the resultant data values from atleast two metrics. Based on these ranges, the resultant data values fromthe metrics may be normalized to correspond to values between zero (0.0)and one (1.0). Likewise, the user of the ECU 100 may assign a weight toeach metric to achieve an appropriate blend of influence. Next, thenormalized values may be combined by the following equation:C=W ₁ ×M ₁ +W ₂ ×M ₂where C represents the new composite value, W_(1,2) represent weightsassigned to each metric, and M_(1,2) represent scalars of the primarymetrics.

A similar combination can be made with vector metrics by taking, in amost basic form, the dot product of two primary metrics. Low or negativevalues in the dot product may indicate substantial disagreement and theuntrustworthiness of sites involved with arrhythmia breakout,disappearance, or ectopy.

Similar to the practice of weighting some metrics more than others inmetric combinations, the ECU 100 may weight some EP data more so thanother EP data. Mapped data values may be weighted, for example, byproximity to various anatomical or functional anatomical structures.This may be the case whether the mapped data values are computed from anindividual metric, a derivative metric, or a combination of metrics. Asa further example, in the case of contact mapping catheters, the ECU 100may receive data regarding which electrodes 30 are sufficientlyproximate to the tissue 12. The ECU 100 may weight the electrodes 30that are in sufficient proximity to the tissue 12 more heavily thanthose electrodes 30 that are not sufficiently proximate to the tissue12. The weights, however, should add up to one. By weighting metrics,the ECU 100 ensures that only sufficiently reliable data is used inmapping, representing, and analyzing the tissue 12.

As a further aspect of the system 10, EP data may also be used toidentify a depolarization wavefront pattern on the tissue 12. The ECU100 may even be configured to recognize multiple wavefront patterns thatmay exist during cardiac activation, especially with complexarrhythmias. Identifying wavefront patterns can be important because itcan lead a user to areas of interest along the tissue 12. Depolarizationwavefronts may, in some cases, reach some locations in the tissue 12that are further away from the source of the wavefront sooner thanlocations that are closer to the source (e.g., because of a shortcircuiting effect). In some of these cases where a point on the tissue12 experiences two wavefronts, it may seem as if there is more than onesource of depolarization.

In other cases, two or more sources of depolarization wavefronts mayactually exist. Normal depolarization waves originate from cells ineither the sinoatrial node, the atrio-ventricular node, the Bundle ofHis, or Purkinje fibers. Though not typical, cardiac muscles are alsocapable of producing electrical impulses that turn into depolarizationwaves. Thus more than one depolarization wave may traverse the tissue12. Identifying sites of arrhythmia breakout allows the user to targetthis location for treatment.

As shown in FIG. 12, several exemplary wavefront patterns may include,for example and without limitation, planar 190, wavebreak 192, collision194, focus 196, short radius entry/reentry 198, and CFEs 200. Additionalpatterns (not shown) may include, for example and without limitation,pivot and rotor wavefronts. To classify the wavefront pattern, the ECU100 may algorithmically combine the known spatial electrode 30configuration of the distal end 28, the position coordinates of thedistal end 28, the electrograms, and timing data.

Wavefront pattern recognition may be implemented using matched filters.Although the algorithm is described in the 2D coordinate system, it canbe extended to a 3D coordinate system using linear transformation. Let{(x₁, y₁), . . . , (x_(n), y_(n))} be the coordinates of some n sensorsdefined in the manufacturing specification. Let {(

₁, ŷ₁), . . . , ({circumflex over (x)}_(n), ŷ_(n))} be the coordinatesof n sensors measured in the ECU 100. Let P¹={θ₁ ¹, . . . , θ_(n) ¹}, .. . P^(m)={θ₁ ^(m), . . . , θ_(n) ^(m)} be m patterns, and θ_(i) ^(j)stands for cardiac direction of the i^(th) sensor on the j^(th) pattern.Let {circumflex over (P)}={{circumflex over (θ)}₁, . . . , {circumflexover (θ)}₂} be the cardiac activation direction of sensors measured inthe ECU 100. The matching process can be implemented as follows:

-   -   Find the center of {(x₁, y₁), . . . , (x_(n), y_(n))} and {(        ₁, ŷ₁), . . . , ({circumflex over (x)}_(n), ŷ_(n))} by        averaging, making sure both coordinates have the same center.    -   Rescale {(x₁, y₁), . . . , (x_(n), y_(n))} and {(        ₁, ŷ₁), . . . ({circumflex over (x)}_(n), ŷ_(n))} so that they        will have the same scales. After shifting and scaling, the only        degree of freedom is the rotation angle α. Let {(        ₁ ^(t), ŷ₁ ^(t)), . . . , ({circumflex over (x)}_(n) ^(t), ŷ_(n)        ^(t))} be the coordinates after modifying the shift and scales        of {(        ₁, ŷ₁), . . . , ({circumflex over (x)}_(n), ŷ_(n))}.    -   Start at α₀ and increase by δ_(α) at the t^(th) step.        -   Rotate coordinates in {((            ₁ ^(t), ŷ₁ ^(t)), . . . , ({circumflex over (x)}_(n) ^(t),            ŷ_(n) ^(t))} by α₀+tδ_(α).        -   Find (            _(i) ^(t), ŷ_(i) ^(t)), corresponding coordinates on the            model (x_(j), y_(j)).        -   Compute the summation of difference diff(t,k)=Σ|θ_(i)−θ_(j)            ^(k)| between the measured pattern and the k^(th) predefined            pattern.    -   Select the pattern that has the minimum diff(t,k), ∀t,k.

This method is just one example of how to determine the optimum patternin real-time. And further, δ_(α) can be adjusted to tradeoff theperformance for computational cost.

As this input is acquired and compiled (e.g., into matrices ordatabases), the ECU 100 may continuously document the comparative timingdifferences between each neighboring electrode 30 and apply a number offilters to this compiled data. The filters may be looking for patternsin these compilations of data corresponding to wavefront patterns, someof which are shown in FIG. 12. These filters may be matched spatialfilters in some embodiments, and may be configured to recognize patternsin the compilations in a variety of directions, orientations, sizes,etc. By using filters on this set of input data, the ECU 100 canidentify and classify various depolarization wavefront patternstraveling across the tissue 12.

Using the various computed metrics and wavefront patternclassifications, the ECU 100 may identify and highlight on the displaydevice 102 an anatomical site that is suspected of maintaining atrialfibrillation, as described below. Moreover, the ECU 100 may compute anddisplay such visual aids in both 2-D and in 3-D. Visual aids in 3-D maybe particularly helpful for areas of the tissue 12 that are damaged.

In an exemplary embodiment, the spot electrodes 30 _(B) and the distalring electrode 30 _(C) of the ablation tip 80 shown in FIG. 5 may besaid to be in a tetrahedron-like arrangement, where the distal ringelectrode 30 _(C) is represented by a geometric centroid. Thistetrahedron-like arrangement allows local bipolar electrogram signals tobe spatially resolved into 3-D space with respect to the ablation tip80. From this set of electrode data, which may not necessarily beorthogonal, a linear transformation may put the bipolar vectorelectrograms into a 3-D orthogonal coordinate frame, as shown in FIG.13, based on the ablation tip 80. In an alternative embodiment, the 3-Dvector electrogram may be associated to the geometrical anatomical model120 since the electrode orientation and position of the ablation tip 80are also known. Other configurations can also be used to obtain similarresults.

Referring to the 3-D graph in FIG. 13, the electrical potentialmagnitude (measured in milli-volts (mV)) of the tetrahedron-likeelectrode arrangement from the ablation tip 80 is shown with respect tothree different directions (v1, v2, and v3). The 3-D vector electrogramspends time near an origin 210 during isoelectric periods and shoots outin various directions with depolarization and repolarization. One loop212 is the result of an ectopic beat, and its depolarization directionis predominantly in the negative v2 direction. Another loop 214 shows astable baseline cardiac rhythm having a depolarization direction insteadin a predominantly positive v1 direction. To acquire data from normaland ectopic beats, the ablation tip 80 may be left at a location in oron the tissue 12 until both states of arrhythmia and non-arrhythmiaoccur.

In addition to the dominant loops, other deflections may exist. Somedeflections, such as the deflection 216, may result from a far-fielddepolarization or near-field repolarization. Low amplitude fractionatedactivity appear as small, irregular deflections around the isoelectricpoint, such as the deflection 218. Still other data, such as thatoriginating from locations near the center of a rotor wavefront, mayappear on this type of graph as a rotating trajectory, “orbiting” aroundan isoelectric point.

Still other aspects of the system 10 relate to the display device 102and to preventing data of a marginal quality from being used by a useror the ECU 100.

The ECU 100 may include another exemplary quality control feature inaddition to, or in place of, the quality control measure, as describedwith reference to FIGS. 10a-10b , of limiting the display ofrepresentation 180 until the distal end 28 is sufficiently close to thetissue 12 such that the metrics are computed based on quality EP data.Accordingly, as shown in FIG. 14a , the ECU 100 may display, in relationto the geometrical anatomical model 120, only those portions of thedistal end 28 that are positioned sufficiently close to the tissue 12.In addition, when the distal end 28 is not sufficiently close to thetissue 12 to measure quality EP data, portions 230 of the distal end 28may be totally transparent or translucent. As the distal end 28approaches the tissue 12, portions 230 may become less transparent, asshown in FIG. 14b . Displaying only sufficiently close portions 230and/or using transparency can signal to the user whether the distal end28 is sufficiently close to the tissue 12.

The embodiment shown in FIG. 14b depicts out-of-range colors 232 thatmay be chosen to indicate values that are out of a range of interest. Itmay be helpful to additionally, or in the alternative, associate contourlines of constant index value with the geometrical anatomic model 120.Contour lines can highlight the boundaries between areas displayingdifferent values. In the case of activation time mapping, for example,these contour lines are referred to as isochrones 234. Likewise, it maybe helpful to superimpose numeric values 236 to the geometricalanatomical model 120 to help identify displayed data values withincertain colored and/or non-colored regions without reference to a legend238. In addition, or in the alternative, the legend 238 may indicate therange of values that correspond to the various displayed colors. The ECU100 may also show on the display device 102 the inclusion of markers,labels, or annotations, some of which may be user-authored, to helptrack prior ablation sites or anatomic or functional locations ofrelevance to electrophysiology procedures, for example. Further examplesmay include marking sites of blockage, slow conduction, low voltage, andfractionation.

As the distal end 28 approaches the tissue 12, the ECU 100 can use morereliable EP data to perform more computations and provide the user withmore information. FIG. 14c shows a representation in the form of avector 240 indicating the general direction and velocity magnitude ofdepolarization conduction traversing the tissue 12. The vector 240 mayrepresent the wavefront for one electrode 30, for a subset of electrodes30, for all electrodes 30 on the distal end 28, or for electrodes 30from distal ends of numerous catheters. Wavefronts having largeconduction velocities may be shown with larger vectors 240, thoughwavefronts having small conduction velocities may be shown with smallervectors 240. As the distal end 28 approaches the tissue 12, the ECU 100may generate the vector 240 by computing metrics based on thecomparative timing and voltage differences between each neighboringelectrode 30. FIG. 14d shows how the ECU 100 may provide even moredetail related to a possible wavefront pattern 242 as additional EP datais measured from a position of the distal end 28 that is closer to thetissue 12.

As described above, the ECU 100 may be configured to prevent data of amarginal quality from reaching display device 102. In such anembodiment, the ECU 100 prevents decisions from being made based on dataof a marginal quality. Instead, the ECU 100 encourages users to obtainadditional good data when needed. Similarly, in metric calculation, theECU 100 may be configured to not use data below a certain confidencethreshold. For example, upon the receipt of proximity data, the ECU 100may discard data acquired from a distance that is too remote from thetissue 12. Under this mode, the display device 102 would only displaydata from a qualified subset of all EP data.

As also discussed above, in some embodiments, the anatomical model 120and HD surface maps, such as the vector 240 or the wavefront 242, may beupdated with each subsequent reference signal trigger. In otherembodiments, the anatomical model 120 and HD surface maps may berefreshed instantly, or as fast as the system 10 will allow.

While the physical screen of the display device 102 may be refreshedfrequently, visual aids (e.g., HD surface maps, markers, labels,annotations, and the like) may persist on the display device 102 forperiods of time. A user may control through the user input devicewhether and how long visual aids may persist. In some embodiments,visual aids may remain visible even after the distal end 28 has movedaway from an area of the tissue 12. In other embodiments, however, thevisual aid may only persist on the display device 102 while the distalend 28 remains within a certain area of the tissue 12. In yet anotherembodiment, the user may configure the system 10 such that visual aidsremain visible on the anatomical model 120 for a fixed amount of timeafter the distal end 28 has moved away from the tissue 12. Still anotherembodiment may involve maintaining only certain visual aids once thedistal end 28 has moved away from a location of the tissue 12. Forexample, it may be helpful to maintain only user-authored annotations onthe display device 102 as regions of data are spatially cataloged. As afurther example, only computed conduction vectors and wavefront patternclassifications, such as those in FIGS. 10b and 12, may be maintained onthe display device 102.

The system 10 is unlike conventional sequential activation mappingsystems, particularly in embodiments where electrograms, metrics, HDsurface maps, and generally EP data from the tissue 12 are catalogedonto the same geometrical anatomical model 120. For example, onedifference is that as regional acquisitions are taken with the distalend 28, the nature of the multiple HD surface maps being compiled can beasynchronous. In other words, unlike conventional synchronousprocedures, the system 10 does not require a fixed reference to whichactivation times of regional acquisitions measured during differentcardiac phases are absolutely indexed. Accordingly, all activation dataneed not be fiducially aligned. Instead, each regional acquisition mayinclude information regarding relative timing differences within thatregion.

The multiple HD surface maps from different regions may be cataloguedasynchronously because oftentimes in complex arrhythmias, numerousdepolarizations occur, as described above. During atrial fibrillation,for example, pathologic electrograms very often contain splitpotentials, mid diastolic potentials, and low voltage fractionateddepolarizing activity. The ECU 100 may prevent any overlap betweenregions as regional EP data acquisitions are spatially cataloged sinceactivation patterns from one region to another may be asynchronous. Itfollows that maintaining the display of regional boundaries or evenlines of block (i.e., separating two adjacent sampled regions by atleast fifty milliseconds of delay) may also be desirable.

With respect to various HD surface maps, such as, AAT mapping andwavefront pattern mapping, for example and without limitation, a beatbuffer may be provided where the user can view how a map changes fromone beat to the next and so on. To illustrate with wavefront mapping,the user may select a timeframe in which to sample EP data. In oneexemplary embodiment, the timeframe may be, for example, ten seconds.The ECU 100 may then input this data into a number of algorithms,resulting in computed wavefront patterns that may be displayed as HDsurface maps, such as the map of 2-D vectors 182 in FIG. 10b , forexample. After computing such patterns for the number of beats thatoccur during this ten second timeframe, the user could view this tensecond sequence of beats on the display device 102. The user could notethe changes, cycles, trends, etc. that occur from one beat to the nextduring this timeframe. The ECU 100 may provide an option for the user toaccelerate or decelerate this viewable sequence. The ECU 100 may alsoprovide an option where the user would need to interact with the displaydevice 102 each time the user desires to advance the view to the nextsequential heartbeat. For example, before advancing to each successivesequence, the GUI 122 could prompt the user to advance the sequence viathe user input device. Moreover, because of the high density ofelectrodes 30 disposed on the distal end 28, the entire HD surface mapmay update with each beat. Still further, this feature may beparticularly helpful when ablation therapy is being performed becausecharacteristics affecting depolarization paths may be affected by thetreatment.

Another aspect of the system 10 involves providing the user withinformation on the display device 102 that may help direct placement ofablation lesions or more definitive diagnostic maneuvers including, forexample, therapy delivery and pace mapping. Therapy delivery may involvea procedure such as, for example, an ablation procedure, and pacemapping may involve reproducing a cardiac activation sequence generatedby a particular arrhythmia. In manual interventions such as these, theacquired EP data may originate from the therapy delivery instrument orthe pacing instrument. The system 10 may utilize the various computedmetrics, composite maps, other visual aids, and quality control features(e.g., transparency) based on the therapy delivery instrument or thepacing instrument to help guide a user through a procedure.

The user may select an ablation or definitive diagnostic targetingcriterion appropriate for the arrhythmia, chamber, and clinicalsituation, which may include appropriate threshold values, map type, andcomposite indices, for example. A field of many arrows describingpatterns of conduction and electrogram amplitude, or activation data,may be condensed, if needed, to a more specific graphical display ofbreakout or other target sites on the geometrical anatomical model 120.To use a condition of breakout as an example, one composite arrow may bederived from the mean direction and amplitude associated with the timeof breakout. Uncertainty of data may be considered by introducing aweighted average that depends on the density of data, its signalquality, and agreement between amplitude and activation time gradients.The tail of this composite arrow may incorporate a target designationand may be superimposed over the geometrical anatomical model 120 whereone or both gradients have large magnitudes. This may be achieved, forexample, by computing a gradient magnitude centroid or finding a pointof maximal gradient magnitude. The direction of the arrow may denote thepredominant depolarization wavefront direction and/or direction ofelectrogram amplitude growth.

In an alternative embodiment, the target site may be designated by othervisual cues such as coloring or texturing of nearby cardiac surfaces.This composite target arrow may be updated as often as on a beat-to-beatbasis. This may be helpful especially when ablation is performedmid-procedure because after ablation, new EP data may be measured,processed, and incorporated into the existing composite target arrowand/or other HD surface maps. On the other hand, the composite targetarrow may be updated at the discretion of the user. Further, the usermay cause the target arrow to become stationary to facilitatecomparisons and other evaluations.

In an exemplary embodiment, the ablation tip 80 of the distal end 28 mayfind particular use for the guidance features of the display device 102.FIG. 15 shows the positions of two ablation tips 80, particularly theablation tip electrodes, which are superimposed onto the geometricalanatomical model 120 as projections 260. In an alternative embodiment,the projections 260 could represent a centroid of the tetrahedron-likearrangement of spot electrodes 30 _(B) and the distal ring electrode 30_(C). Moreover, the centroid of the tetrahedron-like arrangement couldalso serve as a point that is represented by the 3-D map shown in FIG.13. In any event, display of target arrows 262 may signify, for example,the mean direction and amplitude of a recent activation. The arrows 262may not necessarily carry an explicit target, but the size of the arrows262 may confer proximity to a target since the set of bipolar signalsthemselves depend on amplitude gradients and, via filtering, the speedof conduction.

Another advantage to the guidance features of the system 10 is thatautomated initial lesion placement or diagnostic mapping, for example,may also be facilitated. The distal ends 28 having the ablation tips 80may be moved about, and through observation or map generation, an idealposition and values for ablation or pacing may be highlighted. Forexample, the ECU 100 may identify areas of scar tissue based on theresults of computed metrics. Depending on the sizes and locations ofscar tissue identified, the ECU 100 may highlight locations and proposeintensities for corrective and/or preventative procedures. This featurecould assist in mapping, planning, initial ablation therapy delivery, ormore definitive diagnostic testing. Moreover, all the while, the HDsurface maps, the arrows 262, and the visual aids in general may beupdated based on the EP data measured by the distal end 28.

In addition to the advantages noted above, the system 10 may be usefulin understanding macro reentrant rhythms in the Left Atrium (LA). Manypatients are prone to developing this rhythm after electrophysiologyprocedures involving Pulmonary Vein Isolation (PVI). As mentioned above,the system 10 and the distal end 28 may help highlight the anatomicalsite within the tissue 12 maintaining the atrial fibrillation. Further,the system 10 may potentially provide localized substrate analysis ofclinical value.

After performing various analyses of the tissue 12, the system 10 mayalso provide the user with options to save various types of work productfor future use. For example, the system 10 may allow the user to saveseries of EP data, to assign names to dynamic maps, to store maps, andto retrieve maps so as to track distinct arrhythmias and the evolutionof procedural progress. The system 10 could present the user with suchoptions through the GUI 122 during or following the procedure(s). Theuser could then utilize the user input device to save data, maps, andthe like to the ECU 100, to some removable computer-readable storagemedium, to a server, or to any other storage device for subsequentretrieval.

It will be appreciated that in addition to the structure of the system10, another exemplary aspect of the present disclosure is a method formeasuring, classifying, analyzing, and mapping spatial patterns in EPdata and for guiding arrhythmia therapy. It will be further appreciatedthat the methodology and constituent steps thereof performed and carriedout by the ECU 100, and described in great detail above, apply to thisaspect of the disclosure with equal force. Therefore, the description ofthe methodology performed or carried out by the ECU 100 set forth abovewill not be repeated in its entirety, rather several exemplary stepswill be reiterated.

Generally, the ECU 100 may acquire a variety of fundamental input datacoming primarily from both the visualization, navigation, and mappingsubsystem 18 and the electrodes 30 of the distal end 28. Various typesof input data may include, for example without limitation, theparticular configuration of the distal end 28, the known electrodespacing of the distal end 28, coordinate positions of the electrodes 30,EP data from the electrodes 30, timing data corresponding to the EPdata, and the geometrical anatomical model 120.

The ECU 100 may then interpolate the input data to the HD grid 144 andcompute a number of scalar metrics. After interpolating and computingresultant metric values, these values may be associated with thegeometrical anatomical model 120 and mapped in HD as a field of scalars.These HD scalar surface maps may include, for example, electrogramvoltages, PP voltage amplitudes, LAT, CFE activity, andcharacterizations of the tissue 12 as assessed by the ECU 100 or a user.

The ECU 100 may also apply 2-D spatial derivative filters, 3-D temporalderivative filters, or 3-D spatial derivative filters to the input dataand/or resultant scalar metric values to further obtain derivative datavalues. For example, after applying the filters, this derivative datamay include 2-D and 3-D electrogram voltage vectors, 2-D and 3-Dconduction velocities, 2-D amplitude gradients, CFE gradients, and 2-Dcharacterizations of the tissue 12 as assessed by the ECU 100 or theuser.

From the derivative data, the ECU 100 may generate HD vector surfacemaps as shown, for example, in FIG. 16. HD vector surface maps mayinclude gradient maps of conduction velocity, activation amplitude, CFEactivity, and characterizations of the tissue 12, for example. In oneembodiment, a gradient map may involve a vector field having numerousscalars pointing in directions of the greatest rate of change in thescalar field of data. The magnitude of such arrows may correspond to therate of change.

As described above, the ECU 100 can use both scalar and vector resultantmetric data values to generate HD surface maps. In another embodiment,the ECU 100 may use these data values to further compute compositemetrics for display as HD composite maps. Scalar values from differentmetrics, which may otherwise be individually mapped as HD scalar surfacemaps, may be combined by normalizing values, weighting each metric asdesired, and combining the values. One way to combine vector values fromdifferent metrics, which may otherwise be individually mapped as HDvector surface maps, is to compute the dot product of the two individualmetrics.

Another aspect of the ECU 100 involves identifying depolarizationwavefront patterns based on the resultant data values used to generateeither HD scalar surface maps or composite maps. The ECU 100 may, in oneembodiment, use a set of matched spatial filters to search in amultitude of directions, orientations, and sizes for patterns in theresultant data values stored in matrices, databases, or the like.

While much of the data displayed through HD surface maps is critical, itmay be desirable to filter out other portions of noncritical data.Therefore, at the discretion of the user, the ECU 100 may be configuredto apply a 2-D spatial gradient filter to the resultant data values usedto generate either HD scalar surface maps or composite maps. A 2-Dspatial gradient filter may be a matched spatial filter with a shortspatial scale to detect critical spatial characteristics such asgradient. For example, a 2-D spatial gradient filter may be applied todata values or a scalar map itself to highlight only areas on thespatial map that are showing a great deal of change (e.g., electrogramvoltage or LAT) over a short distance. In one embodiment, the user mayspecify the rates of change that should be returned from application ofthe filter.

Similarly, the ECU 100 may also apply a 2-D spatial “bridge” filter tothe resultant data values used to generate either HD scalar surface mapsor composite maps. A 2-D spatial bridge filter may be a matched spatialfilter with large spatial scale. A 2-D spatial bridge filter may aim todetect spatial characteristics such as a bridge or isthmus in the datavalues. A rudimentary example of the type of pattern that a 2-D spatialbridge filter may locate is shown by the following:

$\left| \begin{matrix}0 & 1 & 0 \\0 & 1 & 0 \\0 & 1 & 0\end{matrix} \middle| \quad \right.$

Therefore, 2-D spatial bridge filters may be helpful in detectingcertain types of patterns in the EP data. These types of data mayinclude, for example, low voltage areas surrounded by high voltage areasand low voltages surrounded by fixed anatomic block (slow conductionvelocity). 2-D spatial bridge filter may be even more helpful when usedin combination with other filters.

It should be noted that the ECU 100 may obtain the described metrics,maps, and composite maps in more than one way. For example, as opposedto computing the conduction velocity metric as described further above,the ECU 100 may apply a 2-D spatial derivative filter to an LAT map toobtain a conduction velocity map. As a further example, applying a 2-Dspatial derivative filter to the conduction velocity map may result in agradient of the conduction velocity.

With reference to FIG. 17, one embodiment of the system 10 may bedescribed generally as follows. At step 270, at least one of a pluralityof sensors disposed on a distal end of a medical device may measure EPdata. In some embodiments, only one sensor may measure EP data. In otherembodiments, though, more than one or even all sensors on the distal endmay measure EP data. In still further embodiments, sensors from numerousmedical devices may be simultaneously measuring EP data.

At step 272, the ECU may acquire the EP data that is transmitted from atleast one of the sensors. To that end, the sensors may be operably andelectrically coupled to the ECU. Further, the ECU may in some instancescontinuously acquire EP data as measured by the sensors.

Once the ECU has acquired the EP data, the system 10 may at step 274determine positions of one or more of the plurality of sensors disposedon the distal end of the medical device. As described above, the system10 may use the visualization, navigation, and mapping subsystem to helpdetermine the positions of the sensors. The positions are important fora number of reasons, including that the system 10 may associate EP datavalues with particular locations from which the EP data is measured.

At step 276, the system 10 may compute one or more metrics based on theEP data and/or based on the position(s) of the sensor(s) that measuredEP data. For example, the system computes some metrics based on both thespacing between the sensors and the EP data values at those sensors.Other metrics are based solely on the EP data values acquired from oneor more sensors. In any event, the metric(s) may be any one of theexemplary metrics disclosed above or may be a combination or derivationof the metrics described above.

The system 10 at step 278 may be configured to generate a map based onthe position of at least one of the sensors that measured EP data andalso based on either the EP data measured by the sensor or the metriccomputed in step 276. In short, the system 10 may display the EP datavalue(s) or the computed metric value(s) at the location from which thesensor measured the data. Where numerous sensors measure EP data, thesystem 10 may generate maps that depict the spatial variation in EP dataor metric values across or throughout an object from which the EP datawas measured.

As briefly mentioned above, it will be appreciated that additionalfunctionality described in greater detail above with respect to thesystem 10 may also be part of the inventive methodology. Therefore, tothe extent such functionality has not been expressly described withrespect to the methodology, the description thereof above isincorporated herein by reference.

Further, it should be understood that the system 10, and particularlythe ECU 100, as described above may include conventional processingapparatus known in the art, capable of executing pre-programmedinstructions stored in an associated memory, all performing inaccordance with the functionality described herein. It is contemplatedthat the methods described herein, including without limitation themethod steps of embodiments of the invention, will be programmed in apreferred embodiment, with the resulting software being stored in anassociated memory and where so described, may also constitute the meansfor performing such methods. Implementation of the invention, insoftware, in view of the foregoing enabling description, would requireno more than routine application of programming skills by one ofordinary skill in the art. Such a system may further be of the typehaving both ROM, RAM, a combination of non-volatile and volatile(modifiable) memory so that the software can be stored and yet allowstorage and processing of dynamically produced data and/or signals.

Although numerous embodiments of this disclosure have been describedabove with a certain degree of particularity, those skilled in the artcould make numerous alterations to the disclosed embodiments withoutdeparting from the spirit or scope of this disclosure. All directionalreferences (e.g., upper, lower, upward, downward, left, right, leftward,rightward, top, bottom, above, below, vertical, horizontal, clockwise,and counterclockwise) are only used for identification purposes to aidthe reader's understanding of the present disclosure, and do not createlimitations, particularly as to the position, orientation, or use of thedisclosed system and methods. Joinder references (e.g., attached,coupled, connected, and the like) are to be construed broadly and mayinclude intermediate members between a connection of elements andrelative movement between elements. As such, joinder references do notnecessarily infer that two elements are directly connected and in fixedrelation to each other. It is intended that all matter contained in theabove description or shown in the accompanying drawings shall beinterpreted as illustrative only and not limiting. Changes in detail orstructure may be made without departing from the spirit of the disclosedsystem and methods as defined in the appended claims.

What is claimed is:
 1. A method for analyzing electrophysiological (EP)data from a tissue of a body, the method comprising: receivingelectrical signals representative of the EP data from a plurality ofsensors; computing a metric based on the EP data from the plurality ofsensors; and applying a matched filter to one or more of the EP data andvalues of the metric to identify EP patterns on the tissue of the body.2. The method of claim 1, wherein computing the metric based on the EPdata from the plurality of sensors includes computing a derivative of aconduction velocity of a depolarization wave passing between a pair ofthe plurality of sensors.
 3. The method of claim 2, wherein the methodincludes determining at least one of an acceleration and deceleration ofthe depolarization wave based on the derivative of the conductionvelocity.
 4. The method of claim 3, wherein the method includesidentifying an area of interest on the tissue of the body based on anamount of change in the acceleration and deceleration of thedepolarization wave on the tissue of the body.
 5. The method of claim 1,wherein applying the matched filter includes comparing a number of knownpatterns to one or more of the EP data and values of the metric toidentify EP patterns on the tissue of the body.
 6. The method of claim1, further comprising: combining the metric with an additional metric toform a combination metric, wherein the metric and the additional metricare computed from a same area of interest of the tissue of the body; andgenerating a composite map from the combination metric.
 7. The method ofclaim 6, wherein the combination metric comprises a spatial gradient anda temporal gradient.
 8. The method of claim 1, further comprising:generating a map based on a determined position of at least one of theplurality of sensors and the identified EP patterns; and updating themap with successive heartbeats.
 9. The method of claim 1, furthercomprising computing a derivative metric based on the metric.
 10. Themethod of claim 9, further comprising indicating with the derivativemetric at least one rate at which values of the metric are changing inrelation to distance.
 11. A non-transitory computer readable mediumstoring instructions executable by a processing device, to: receiveelectrophysiological data (EP) data associated with a tissue of a bodyfrom a sensor; compute a temporal metric based on the EP data and aposition of the sensor, the position of the sensor being calculatedbased on positional data received from the sensor; generate a map basedon the position of the sensor and on one or more of the temporal metricand the EP data; and apply a matched filter to one or more of the EPdata and values of the temporal metric to identify EP patterns on thetissue of the body.
 12. The non-transitory computer readable medium ofclaim 11, wherein the instructions executable to compute the temporalmetric include instructions executable to compute a value indicating anamount of time that has elapsed since the sensor was last depolarized.13. The non-transitory computer readable medium of claim 11, wherein theinstructions executable to compute the temporal metric includeinstructions executable to compute a value indicating an amount of timethat the sensor spends depolarizing.
 14. The non-transitory computerreadable medium of claim 11, wherein the instructions executable tocompute the temporal metric include instructions executable to compute avalue indicating a summation of amounts of time where at least one of aset of a plurality of sensors spends depolarizing.
 15. A system foranalyzing and mapping electrophysiological (EP) data from a tissue of abody, the system comprising: a processor; and a non-transitory computerreadable medium coupled with the processor, the non-transitory computerreadable medium storing instructions executable by the processor to:receive EP data associated with a tissue of a body from a sensor;compute a plurality of metrics based on the EP data and a position ofthe sensor, the position of the sensor being calculated based onpositional data received from the sensor, wherein the plurality ofmetrics include at least a temporal metric and a spatial metric; combinethe plurality of metrics to form a combination metric by normalizingvalues from at least two of the plurality of metrics and weighting theat least two metrics; and generate a map based on the position of thesensor and based on one or more of the combination metric and the EPdata.
 16. The system of claim 15, further comprising instructionsexecutable by the processor to compute the plurality of metrics from asame area of interest of the tissue of the body.
 17. The system of claim16, wherein the instructions executable by the processor to compute thetemporal metric include instructions executable to determine a metricindicating an amount of time that has elapsed since a most-recentdepolarization wave passed the sensor.
 18. The system of claim 15,wherein the instructions executable by the processor to generate the mapinclude instructions executable to generate an absolute activation timemap.
 19. A system for analyzing and mapping electrophysiological (EP)data from a tissue of a body, the system comprising: a processor; and anon-transitory computer readable medium coupled with the processor, thenon-transitory computer readable medium storing instructions executableby the processor to: receive EP data associated with a tissue of a bodyfrom a sensor; compute a plurality of metrics based on the EP data and aposition of the sensor, the position of the sensor being calculatedbased on positional data received from the sensor, wherein the pluralityof metrics include at least a temporal metric and a spatial metric;combine the plurality of metrics to form a combination metric; generatea map based on the position of the sensor and based on one or more ofthe combination metric and the EP data; and apply a matched filter toone or more of the EP data and values of the combination metric toidentify EP patterns on the tissue of the body.